i
A ROAD NETWORK SHORTEST PATH ANALYSIS: APPLYING TIME-VARYING
TRAVEL-TIME COSTS FOR EMERGENCY RESPONSE VEHICLE ROUTING,
DAVIS COUNTY, UTAH
A THESIS PRESENTED TO
THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES
IN CANDIDACY FOR THE DEGREE OF
MASTER OF SCIENCE
By
MICHAEL T. WINN
NORTHWEST MISSOURI STATE UNIVERSITY
MARYVILLE, MISSOURI
JANUARY, 2014
ii
A ROAD NETWORK SHORTEST PATH ANALYSIS
A Road Network Shortest Path Analysis: Applying Time-Varying
Travel-Time Costs for Emergency Response Vehicle Routing, Davis County, Utah
Michael T. Winn
Northwest Missouri State University
THESIS APPROVED
________________________________________________________________________
Thesis Advisor, Dr. Yi-Hwa Wu Date
________________________________________________________________________
Dr. Patricia Drews Date
________________________________________________________________________
Dr. Ming-Chih Hung Date
________________________________________________________________________
Dean of Graduate School, Dr. Gregory Haddock Date
iii
A Road Network Shortest Path Analysis
Abstract
Rapid emergency response to the scene of a traffic accident and transportation of
the injured to a medical facility is critical for saving lives. Traffic congestion is a major
problem in urban areas and Davis County, Utah is no exception. Traffic congestion can
disrupt emergency response, but dynamic network routing can offer solutions. A GIS can
be a useful tool for determining emergency vehicle response routing, and the application
of dynamic variables like historical traffic count data can help emergency response
vehicles avoid traffic congestion and improve response times.
This research examines a methodology where route solvers based on Dijkstra’s
shortest path algorithm in ArcGIS Network Analyst were utilized to identify the closest
ground emergency response unit (e.g., fire station) and hospital (e.g., trauma center) to
each incident and then solving the shortest path problem centered around emergency
response routing scenarios. Cost attributes or impedances, namely distance, free-flow
travel time and time-varying travel time originating from historical traffic data, were
applied to each routing scenario to determine the shortest, fastest, and best (optimal)
routes from an origin to a destination. The best route is defined as the route with the least
travel cost determined by the impedance applied.
Results were analyzed and compared. Findings based on these routing analyses
show that dynamic time-varying travel time derived from historical traffic count data can
iv
optimize emergency response routing, improve travel times and validate that dynamic
network routing can improve emergency response routing above static networks.
Although challenges and limitations existed in this research, it is believed that future
improvements through the incorporation of live traffic data using GPS technology and
traffic cams could greatly enhance this type of research and assist local public safety and
EMS agencies improve levels of service as population growth and subsequent traffic
congestion increases.
v
Table of Contents
Abstract ........................................................................................................................ iii
List of Figures ............................................................................................................. vii
List of Tables ................................................................................................................. x
Acknowledgments ....................................................................................................... xii
List of Abbreviations ................................................................................................. xiii
Chapter 1: Introduction .................................................................................................. 1
1.1 Research Background ....................................................................................... 1
1.2 Research Objectives .......................................................................................... 3
1.3 Study Area ........................................................................................................ 3
Chapter 2: Literature Review ......................................................................................... 8
2.1 Network Analysis ............................................................................................. 8
2.2 Shortest Path Analysis ...................................................................................... 9
2.3 Dijkstra’s Algorithm ....................................................................................... 10
2.4 Static and Dynamic Networks ........................................................................ 10
2.5 Traffic Congestion and Dynamic Emergency Response Routing .................. 12
2.6 Historical Traffic Profiles ............................................................................... 13
Chapter 3: Conceptual Framework and Methodology ................................................. 15
3.1 Data Sources ................................................................................................... 17
3.2 Data Preparation ............................................................................................. 18
3.2.1 Road Network Centerlines ...................................................................... 18
3.2.2 Road Classifications ................................................................................ 19
3.2.3 Historical Hourly Traffic Volume Data .................................................. 21
3.2.4 Grouping Historical Traffic Volume Data .............................................. 23
3.2.5 Historical Traffic Volume Profiles ......................................................... 25
3.2.6 Modeling Historical Traffic Data ............................................................ 33
3.2.7 Incorporating Historical Traffic Data ..................................................... 35
vi
3.3 Developing the Road Network Model ............................................................ 37
3.3.1 One Way Restrictions ............................................................................. 39
3.3.2 Global Turn Delays ................................................................................. 41
Chapter 4: Analysis and Results .................................................................................. 45
4.1 Routing Example for IN-1 .............................................................................. 50
4.1.1 IN-1: Closest Facility Analysis ............................................................... 50
4.1.2 IN-1: Route Analysis Scenario 1 ............................................................ 58
4.1.3 IN-1: Route Analysis Scenario 2 ............................................................ 71
4.1.4 IN-1: Emergency Response Routing Review .......................................... 80
4.2 Routing Example for IN-2 .............................................................................. 84
4.2.1 IN-2: Closest Facility Analysis ............................................................... 84
4.2.2 IN-2: Route Analysis Scenario 1 ............................................................ 91
4.2.3 IN-2: Route Analysis Scenario 2 ............................................................ 98
4.2.4 IN-2: Emergency Response Routing Review ........................................ 109
4.3 Discussion of Results .................................................................................... 113
Chapter 5: Conclusion and Future Improvements ..................................................... 116
5.1 Conclusion .................................................................................................... 116
5.2 Limitations .................................................................................................... 117
5.3 Challenges and Solutions .............................................................................. 119
5.4 Future Improvements .................................................................................... 120
References .................................................................................................................. 122
vii
List of Figures
Figure 1. Study area, Davis County with Utah inset ....................................................... 4
Figure 2. Road network, Davis County, Utah ................................................................. 6
Figure 3. EMS facilities (ground units) and hospitals, Davis County, Utah .................. 7
Figure 4. Methodology flow chart ................................................................................. 16
Figure 5. Road network and the Urban Area Functional Classification system ............ 20
Figure 6. Geographic locations of the ATR sites ........................................................... 22
Figure 7. ATR site 0316 traffic volume profile - Tuesday average, April 2010 ............ 26
Figure 8. ATR site 0316 traffic volume profile – Saturday average, April 2010 .......... 26
Figure 9. ATR site 0316 traffic volume profile – Sunday average, April 2010 ............ 26
Figure 10. ‘DailyProfiles_Time_60min’ table: Profile 3 ............................................... 29
Figure 11. ‘DailyProfiles_Time_60min’ table: Profile 8 ............................................... 29
Figure 12. ‘DailyProfiles_Time_60min’ table: Profile 12 ............................................. 29
Figure 13. ‘DailyProfiles_Time_60min’ table: Profile 14 ............................................. 30
Figure 14. ‘DailyProfiles_Time_60min’ table: Profile 21 ............................................. 30
Figure 15. ‘DailyProfiles_Time_60min’ table: Profile 91 ............................................. 30
Figure 16. ‘DailyProfiles_Time_60min’ table: Profile 92 ............................................. 31
Figure 17. ‘DailyProfiles_Time_60min’ table: Profile 96 ............................................. 31
Figure 18. ‘DailyProfiles_Time_60min’ table: Profile 98 ............................................. 31
Figure 19. Network dataset properties associated with the historical traffic tables ....... 36
Figure 20. Assignment of network attributes ................................................................. 36
Figure 21. File geodatabase data model ......................................................................... 38
Figure 22. Correct one-way travel, from Incident 1 to Ogden Regional Medical
Center ........................................................................................................... 40
Figure 23. Incorrect one-way travel, from Incident 1 to Ogden Regional Medical
Center ........................................................................................................... 40
Figure 24. Turn categories available for various road types .......................................... 42
Figure 25. Global turn delay default settings ................................................................. 43
viii
Figure 26. Global turn delay customized settings .......................................................... 43
Figure 27. Example of routing scenarios S1 and S2 ...................................................... 46
Figure 28. Route analysis flowchart .............................................................................. 47
Figure 29. Analysis settings available for ‘Closest Facility’ solver .............................. 51
Figure 30. Analysis settings available settings for ‘Route’ solver ................................. 51
Figure 31. Routes from nearest ground unit to IN-1 applying DIST impedance .......... 54
Figure 32. Routes from nearest ground unit to IN-1 applying FFTT impedance .......... 55
Figure 33. Routes from nearest ground unit to IN-1 applying TVTT impedance ......... 55
Figure 34. Routes from IN-1 to nearest hospital applying DIST impedance ................ 56
Figure 35. Routes from IN-1 to nearest hospital applying FFTT impedance ................ 57
Figure 36. Routes from IN-1 to nearest hospital applying TVTT impedance ............... 58
Figure 37. IN-1, Scenario 1, Sunday travel time profile, TVTT impedance ................. 63
Figure 38. IN-1, Scenario 1, Tuesday travel time profile, TVTT impedance ................ 64
Figure 39. IN-1 Scenario 1, Route A ............................................................................. 65
Figure 40. IN-1 Scenario 1, Route B ............................................................................. 65
Figure 41. IN-1, Scenario 2, Sunday travel time profile, TVTT impedance ................. 73
Figure 42. IN-1, Scenario 2, Tuesday travel time profile, TVTT impedance ................ 74
Figure 43. IN-1 Scenario 2, Route A ............................................................................. 75
Figure 44. IN-1 Scenario 2, Route B ............................................................................. 75
Figure 45. IN-1 Scenario 2, Route C ............................................................................. 76
Figure 46. IN-1, combined scenarios, Sunday and Tuesday, DIST impedance ............ 81
Figure 47. IN-1, combined scenarios, Sunday and Tuesday, FFTT impedance ............ 81
Figure 48. IN-1, combined scenarios, Sunday, TVTT impedance ................................ 82
Figure 49. IN-1, combined scenarios, Tuesday, TVTT impedance ............................... 82
Figure 50. Routes from nearest ground unit to IN-2 applying DIST impedance ........... 85
Figure 51. Routes from nearest ground unit to IN-2 applying FFTT impedance ........... 86
Figure 52. Routes from nearest ground unit to IN-2 applying TVTT impedance .......... 87
ix
Figure 53. Routes from IN-2 to nearest hospital applying DIST impedance ................. 88
Figure 54. Routes from IN-2 to nearest hospital applying FFTT impedance ................. 89
Figure 55. Routes from IN-2 to nearest hospital applying TVTT impedance ................ 90
Figure 56. IN-2 Scenario 1, Sunday travel time profile, TVTT impedance ................... 93
Figure 57. IN-2 Scenario 1, Tuesday travel time profile, TVTT impedance .................. 94
Figure 58. IN-2 Scenario 1, Route A .............................................................................. 95
Figure 59. IN-2 Scenario 1, Route B .............................................................................. 95
Figure 60. IN-2 Scenario 2, Sunday travel time profile, TVTT impedance .................. 100
Figure 61. IN-2 Scenario 2, Tuesday travel time profile, TVTT impedance ................. 101
Figure 62. IN-2 Scenario 2, Route A ............................................................................. 102
Figure 63. IN-2 Scenario 2, Route B ............................................................................. 102
Figure 64. IN-2 Scenario 2, Route C ............................................................................. 103
Figure 65. IN-2 Scenario 2, Route D ............................................................................. 103
Figure 66. IN-2 Scenario 2, Route E.............................................................................. 104
Figure 67. IN-2, combined scenarios, Sunday and Tuesday, DIST impedance ............ 110
Figure 68. IN-2, combined scenarios, Sunday and Tuesday, FFTT impedance ............ 110
Figure 69. IN-2, combined scenarios, Sunday, TVTT impedance ................................ 111
Figure 70. IN-2, combined scenarios, Tuesday, TVTT impedance ............................... 111
x
List of Tables
Table 1. Urban Area Functional Classification system ................................................. 20
Table 2. ATR sites associated with the Functional Classification system ..................... 22
Table 3. April 2010 traffic volumes for ATR site 0316 ................................................ 23
Table 4. April 2010 traffic volumes for ATR site 0316, grouped ................................. 24
Table 5. ‘DailyProfiles_Time_60min’ file geodatabase table ....................................... 28
Table 6. Profile IDs from the ‘DailyProfiles_Time_60min’ table ................................. 28
Table 7. 'Project_Profiles' file geodatabase table ........................................................... 32
Table 8. 'ProjectArea' feature class attribute table ......................................................... 32
Table 9. ‘Global Turn Delay’ directions and penalty values in seconds ....................... 42
Table 10. Incident information from 2010 UDOT crash statistics ................................ 46
Table 11. Analysis settings for finding nearest ground unit to IN-1 ............................. 53
Table 12. Analysis settings for finding nearest hospital from IN-1 ............................... 53
Table 13. Results for finding nearest ground unit to IN-1 ............................................. 54
Table 14. Results for finding nearest hospital from IN-1 .............................................. 54
Table 15. Analysis settings used for S1 ......................................................................... 61
Table 16. Scenario 1, Sunday, Clinton FD to IN-1, DIST impedance .......................... 61
Table 17. Scenario 1, Tuesday, Clinton FD to IN-1, DIST impedance ......................... 61
Table 18. Scenario 1, Sunday, Clinton FD to IN-1, FFTT impedance .......................... 62
Table 19. Scenario 1, Tuesday, Clinton FD to IN-1, FFTT impedance ........................ 62
Table 20. Scenario 1, Sunday, Clinton FD to IN-1, TVTT impedance ......................... 63
Table 21. Scenario 1, Tuesday, Clinton FD to IN-1, TVTT impedance ........................ 64
Table 22. IN-1 Scenario 1, Sunday, comparison of cost impedance between Routes
A and B ........................................................................................................ 70
Table 23. Scenario 2, Sunday, IN-1 to Davis Hospital, DIST impedance ..................... 71
Table 24. Scenario 2, Tuesday, IN-1 to Davis Hospital, DIST impedance ................... 71
Table 25. Scenario 2, Sunday, IN-1 to Davis Hospital, FFTT impedance .................... 72
xi
Table 26. Scenario 2, Tuesday, IN-1 to Davis Hospital, FFTT impedance ................... 72
Table 27. Scenario 2, Sunday, IN-1 to Davis Hospital, TVTT impedance ................... 73
Table 28. Scenario 2, Tuesday, IN-1 to Davis Hospital, TVTT impedance .................. 74
Table 29. IN-1 Scenario 2, Tuesday, comparison of cost impedance between Routes
B and C ........................................................................................................ 79
Table 30. IN-1, combined scenarios, comparison of emergency response routes ......... 83
Table 31. Results for finding nearest ground unit to IN-2 .............................................. 84
Table 32. Results for finding nearest hospital from IN-2 ............................................... 84
Table 33. Scenario 1, Sunday, Kaysville FD to IN-2, DIST impedance ........................ 91
Table 34. Scenario 1, Tuesday, Kaysville FD to IN-2, DIST impedance....................... 91
Table 35. Scenario 1, Sunday, Kaysville FD to IN-2, FFTT impedance ........................ 92
Table 36. Scenario 1, Tuesday, Kaysville FD to IN-2, FFTT impedance ...................... 92
Table 37. Scenario 1, Sunday, Kaysville FD to IN-2, TVTT impedance ....................... 93
Table 38. Scenario 1, Tuesday, Kaysville FD to IN-2, TVTT impedance ..................... 94
Table 39. IN-2 Scenario 1, Tuesday, comparison of cost impedance between Routes
A and B ......................................................................................................... 97
Table 40. Scenario 2, Sunday, IN-2 to Davis Hospital, DIST impedance ...................... 98
Table 41. Scenario 2, Tuesday, IN-2 to Davis Hospital, DIST impedance .................... 98
Table 42. Scenario 2, Sunday, IN-2 to Davis Hospital, FFTT impedance ..................... 99
Table 43. Scenario 2, Tuesday, IN-2 to Davis Hospital, FFTT impedance .................... 99
Table 44. Scenario 2, Sunday, IN-2 to Davis Hospital, TVTT impedance ................... 100
Table 45. Scenario 2, Tuesday, IN-2 to Davis Hospital, TVTT impedance .................. 101
Table 46. IN-2 Scenario 2, Sunday, comparison of cost impedance between Routes
A, B, and C................................................................................................... 107
Table 47. IN-2 Scenario 2, Tuesday, summary of cost impedance between Routes
A, B, C, D, and E ......................................................................................... 108
Table 48. IN-2, combined scenarios, comparison of emergency response routes ......... 112
xii
Acknowledgements
I would first like to thank my thesis advisor, Dr. Yi-Hwa Wu, for her patience and
support throughout this research process. Her advice and understanding of the subject
matter was invaluable. I would like to thank my academic advisor, Dr. Patricia Drews,
who not only helped me with this research, but for over eight years guided and
encouraged me through the GIScience Master’s program. I would also like to thank Dr.
Ming-Chih Hung for his much appreciated assistance as well.
Other individuals and agencies I would like to acknowledge are Mike Price with
Entrada/San Juan, Inc. Nicolas Virgen, Scott Jones, Danielle Herrscher, and Brandi
Trujillo with the Utah Department of Transportation. Bert Granberg and his staff with
the Utah Automated Geographic Reference Center. Joshua Legler and Robert Jex with
the Utah Bureau of Emergency Medical Services. Mike King with the Hill Air Force
Base Fire Department and Patrick McDonald with the Layton City Fire Department. I
want to thank them for generously sharing information, their time, and their insight for
this research.
Lastly, I would like to thank my family for their patience and understanding over
the years. I would especially like to thank my wife Linda, for her love and support
during this long undertaking. Without her strength and encouragement, my educational
goals and this research would not have been possible.
xiii
List of Abbreviations
AGRC: Utah Automated Geographic Reference Center
ATR: Automatic Traffic Recorder
BEMS: Utah Bureau of Emergency Medical Services
DIST: Distance cost attribute or impedance
EMS: Emergency Medical Services
Esri: Environmental Systems Research Institute
FC: Functional Classification (Urban area functional classification system)
FFTT: Free-Flow Travel Time
FGDB: File Geodatabase
GIS: Geographic Information System
GIS-T: Geographic Information Systems for Transportation
GTD: Global Turn Delays
HAFB: Hill Air Force Base
NA: Esri Network Analyst
ND: Network Dataset
NHTSA: National Highway Traffic Safety Administration
TVTT: Time-Varying Travel Time
UDOT: Utah Department of Transportation
1
Chapter 1: Introduction
Emergency medical services (EMS) is a system that provides emergency medical
care. Once it is activated by an incident that causes serious illness or injury, the focus of
EMS is the emergency medical care and the patient(s). Another element of the EMS is
the ground or air transportation of the patient(s) to a hospital or trauma center (National
Highway Traffic Safety Administration Emergency Medical Services [NHTSA EMS]
2013). EMS response time is critical in emergency requests involving injury (Panahi and
Delavar 2009). Technological advances such as geographic information systems (GIS),
can allow emergency vehicles to reach patients more quickly (Wilde 2009), and
efficiency in routing emergency fire and medical vehicles to a traffic incident is critical
for saving lives (Cova 1999).
1.1 Research Background
A GIS can be used for many roles in emergency management. It is an effective
tool for determining emergency vehicle response routing and solving the emergency
vehicle shortest path routing problem (Alivand et al. 2008, Cova 1999, Panahi and
Delavar 2008). A shortest path algorithm applied to a routing problem in a transportation
network can calculate the path with minimal travel cost or least impedance from an origin
to a destination. Depending on the type of cost, the shortest path can be referred to as the
shortest, fastest, or most optimal path or route. There are several impedance factors that
can affect emergency services and vehicle response times. They include distance, travel
time, and traffic congestion as a result of variations in traffic flow related to the time of
2
day. Traffic congestion is a major problem in urban areas and can disrupt emergency
response (Panahi and Delavar 2008; 2009, Naqi et al. 2010).
In recent years, traffic congestion in Davis County, Utah has become more
problematic and widespread, thus affecting emergency response performance. Traffic
congestion will continue to be a concern as the region grows in population and
congestion increases (Utah Department of Transportation [UDOT] 2008, United States
Census Bureau 2012). East-west transportation is restricted by a narrow urban corridor
and many of the residents commute south to Salt Lake County. From 2000 to 2010,
Davis County experienced a population growth rate of 28.2% and an increase in housing
units by 31.6%, and the average population density per square mile increased by 30.7%
(United States Census Bureau 2012). With no signs of slowing population growth or
opportunities for employment, Davis County must plan for a variety of transportation
facilities such as roads and mass transit systems to accommodate the anticipated growth
(UDOT 2008).
This study selected Davis County, Utah as the case study area because of its
constricted, north/south orientated road system and traffic congestion. Using commercial
ready-to-use GIS software, a dynamic road network was created and a real-world
emergency response routing analysis was performed to determine the shortest, fastest,
and most optimal path or routes for emergency response vehicles by applying different
cost attributes or impedances. An analysis and comparison of the resulting emergency
vehicle routing scenarios was made to demonstrate how routes and travel times are
affected when these cost attributes are applied.
3
1.2 Research Objectives
The overall objective of this research was to observe if routes and response times
for emergency response vehicles change due to variations in traffic flow related to the
day (e.g., weekday or weekend) and the time of day (traffic congestion). Commonly used
shortest path algorithms were used to calculate the shortest, fastest, and the most optimal
path from an emergency response unit (e.g., fire station) to an incident (e.g., car crash)
then to a trauma center (e.g., hospital) by applying three cost attributes or impedances to
road network edges: distance, base travel time or free-flow travel time, and time-
dependent or time-varying travel time originating from historical traffic data. A major
component of this research was the application of historical traffic data. To perform this
analysis, traffic volume profiles based on Utah Department of Transportation (UDOT)
traffic count data were created and applied as a network cost attribute. Dynamic routing
based on cost attributes derived from historical travel-time data and applied to network
edges should help response vehicles avoid congested areas and improve travel times (Kok
et al. 2012, Panahi and Delavar 2009).
1.3 Study Area
Davis County was founded in 1850 and is situated in north central Utah (Figure
1). The Wasatch Range borders the east side of the county and the Great Salt Lake
borders the west side. Weber County is located to the north of Davis County with the
Weber River delineating part of the northern county line while Salt Lake County borders
on the south. Davis County has 15 incorporated cities and towns (Figure 1) and a total
population of 306,500 (United States Census Bureau 2012). Lands outside these
4
incorporated cities are primarily uninhabited wetlands, desert or mountainous areas. The
county seat is located in the city of Farmington which is located about mid-point in the
county. Davis County covers about 635 square miles with the Great Salt Lake occupying
more than half of this area. Hill Air Force Base (HAFB) is located entirely within the
northern part of the county and is the home of the Ogden Air Logistics Center (OALC)
which serves primarily as a repair facility for military aircraft (Davis County Emergency
Management Services 2009).
Figure 1. Study area, Davis County with Utah inset
5
An interstate highway (I-15) and a railroad system traverse the entire length of the
County and provide the only major access and egress route for the County. Davis County
contains 1,776 miles of roads mostly in the incorporated areas and includes 1,704 miles
of paved roads and 72 miles of dirt/4wd roads (Figure 2). There are 84 miles of federal
highways, 225 miles of state routes, 1,357 miles of local roads and 38 miles of access
ramps (Utah Automated Geographic Reference Center [Utah AGRC] 2012). It should be
noted that Figure 2 does not show the entire road network created for this research
project.
The study area is served by ten EMS agencies not including HAFB, four
designated emergency medical dispatch agencies and seventeen EMS facilities or ground
units not including HAFB (Utah AGRC 2012, Utah Bureau of Emergency Medical
Services [Utah BEMS] 2012a; b). There are four hospitals located in Davis County, two
of which are designated as resource hospitals that have emergency rooms staffed with
24/7 physicians (Figure 3). There are four Level I (highest level of care) trauma centers
located in the northern portion of Salt Lake County (Salt Lake City) within
approximately 8 miles of the southern border of Davis County and two Level II trauma
centers located in Ogden within 4 miles of the northern border of Davis County (Utah
AGRC 2012, Utah BEMS 2012c).
6
Figure 2. Road network, Davis County, Utah
7
Figure 3. EMS facilities (ground units) and hospitals, Davis County, Utah
8
Chapter 2: Literature Review
Geographic Information Systems for Transportation (GIS-T) represents one of the
most important application areas of GIS technology (Goodchild 2000). Shaw (2010)
referred to GIS-T as the application of information technology to the transportation
problem. Abkowitz et al. (1990) stated over two decades ago that the field of
transportation was inherently geographic and GIS was a technology with considerable
potential for achieving gains in efficiency and productivity for many transportation
applications.
2.1 Network Analysis
A background knowledge of a network can be beneficial to the understanding of
transportation network analysis. A network is essentially a set of lines known as
segments or edges connected or joined by a set of vertices known as nodes or junctions.
A GIS stores these edge and junction features with their attributes. Spatio-temporal
networks are networks whose topology and parameters change with time. These
networks are important to applications such as emergency traffic planning and route
finding (George et al. 2007).
Network analysis in GIS has its origins in the mathematical sub-disciplines of
graph theory and topology. An important association between graph theory and a
network is topology. Topological properties such as connectivity, coincidence, and
adjacency are key to network analysis. An important advantage of a GIS-based network
9
in contrast to graph theory is the geographic elements of shape or length. Length is
essential for calculating travel time (Curtin 2007).
The use of GIS for network analysis is essential for improving emergency
response routing based on travel time information (Alivand et al. 2008, Panahi and
Delavar 2008). Curtin (2007) thought network analysis was one of the most significant
research and application areas in GIScience while Sadeghi-Niarki et al. (2011) mentioned
network analysis is a powerful tool in the GIS environment for solving the optimal path
in a network.
2.2 Shortest Path Analysis
A shortest path problem is to find a path with minimum travel cost from one or
more origins to one or more destinations through a network (Lim and Kim 2005, Panahi
and Delavar 2008). Shortest path analysis is important because of its wide range of
applications in transportation (Lim and Kim 2005). Naqi et al. (2010) stated that the
shortest path helps calculate the most optimal route, and optimal routing is the process of
defining the best route to get from one location to another. The best route could be the
shortest or fastest depending on how it is defined.
The shortest path can be computed either for a given start time or to find the start
time and the path that leads to least travel time journeys. The classic shortest path
problem and finding the best route for vehicle routing in static road networks based on
Dijkstra’s algorithm has been examined extensively in the literature over the years
(Alazab et al. 2011, Alivand et al. 2008, Kim et al. 2005). George et al. (2007) claimed
that developing efficient algorithms for computing shortest paths in a time-varying spatial
network can be challenging.
10
2.3 Dijkstra’s Algorithm
Dijkstra’s algorithm or variations of it are the most commonly used route finding
algorithm for solving the shortest path (Sadeghi-Niaraki et al. 2011). Dijkstra's algorithm
is sometimes called the single-source shortest path because it solves the single-source
shortest-path problem on a weighted, directed graph (G = V, E) where
V is a set whose elements are called vertices (nodes, junctions, or intersections) and E is
a set of ordered pairs of vertices called directed edges (arcs or road segments). To find a
shortest path from a source s vertex or location to a destination location d, Dijkstra's
algorithm maintains a set S of vertices whose final shortest-path weights from the source
s have already been determined. Knowing that w is the edge weight, the edge is an
ordered pair (u, v) and assuming w (u, v) ≥ 0 for each edge (u, v) ϵ E, the algorithm
repeatedly selects the vertex u ϵ V – S with the minimum shortest-path estimate, adds u
to S, and relaxes all edges leaving u (Cormen et al. 2001, Puthuparampil 2007).
The commercial GIS software that was used to perform the route analysis for this
study is Esri ArcGIS Network Analyst. ArcGIS is suitable for this kind of research
because it is commercially available, and the Network Analyst extension is included in
the student edition of ArcGIS. The route solver in Network Analyst to determine the
shortest path is based on Dijkstra's algorithm (Karadimas et al. 2007).
2.4 Static and Dynamic Networks
A dynamic network differs from a static network in that travel time changes or
varies with respect to time. Variables used to store the cost of traversing across an edge
11
change with respect to time is a dynamic network. It is important to consider travel time
as a parameter for finding the optimal path in dynamic networks (Alivand et al. 2008).
Recent GIS data models related to GIS-T are basically static in nature. Static
information is not sufficient to estimate travel time, since it does not reflect dynamically
changing traffic conditions. Static information could lead to incorrect shortest paths;
however, if there is a way to obtain the cost in real-time and then apply a time-dependent
shortest path algorithm, it would result in a better solution for the shortest path (Panahi
and Delavar 2009). According to Nadi and Delavar (2003), most conventional GIS data
models are based on a static representation of reality and constrain GIS capabilities for
representation of dynamic information. GIS data models that can represent the dynamic
aspects of transportation challenges are needed to represent and analyze space-time
information (Shaw 2010). Static variables that could be assigned to a road edge or
junction might include distance, speed limits, free-flow travel time, number of lanes, turn
penalties, slope of the road, hierarchical classifications, etc. (Li and Lin 2003, Sadeghi-
Niaraki et al. 2011, Thirumalaivasan and Guruswamy 1997).
In contrast, travel time is considered dynamic due to traffic volume, and historical
traffic data applied to a network can approximate traffic congestion. Dynamic variables
known as costs or weights are time-dependent or time-varying travel times derived from
historical traffic data. Dynamic variables that could be assigned to a road edge or
junction might include weather variables or time-varying travel time derived from traffic
count data (Sadeghi-Niaraki et al. 2011, Thirumalaivasan and Guruswamy 1997). The
network analysis will better reflect actual traffic conditions occurring at various times
12
during the day when time-dependent variables are incorporated (Kok et al. 2012, Panahi
and Delavar 2009).
2.5 Traffic Congestion and Dynamic Emergency Response Routing
There are several factors that can affect emergency services and vehicle response
times. Variations in traffic flow or volume related to time of day is one of them. This is
referred to as traffic congestion. Traffic congestion can have several causes. Some are
predictable such as traffic during daily peak hours and some less predictable such as
weather or accidents. Delays caused by peak hour traffic congestion constitute the
majority of traffic congestion delays (Kok et al. 2012). Delays affecting response times
in emergency services caused by traffic congestion are considered dynamic because they
spread through a network and vary over time (Panahi and Delavar 2009, Riad et al.
2012).
”The increasing ubiquity and complexity of urban congestion combined with its
severe negative impacts suggests the need for new tools to analyze and predict congestion
patterns” like a GIS (Riad et al. 2012, Wu et al. 2001). A critical component in incident
or emergency response actions is to deploy appropriate response units to the incident
scene as quickly as possible (Huang and Pan 2007). According to Panahi and Delavar
(2008; 2009), the problem of traffic congestion in urban areas can influence the travel
times of emergency vehicles, but the development of dynamic routing can offer solutions.
A more recent study (Kamga et al. 2011) showed dynamic traffic models are particularly
appropriate for modeling highway incidents because the timing of incident occurrence,
management, recovery, and the use of alternate routes is critical to roadway performance
13
and driver behaviors. Haghani et al. (2003) argued the purpose of vehicle dispatching is
to minimize the total travel time in the system and that time-dependent shortest path
analysis is useful for the calculation of travel times and can help EMS dispatching and re-
routing by reducing response times and improve services. Dynamic shortest path routing
should improve emergency response times (Panahi and Delavar 2008; 2009).
2.6 Historical Traffic Profiles
Several methods are known to apply historical traffic data to a road network. One
approach is to compute travel times for each road segment, which are then stored as
attributes for each feature. Depending on the sampling rate, storage and duplication
issues can be a concern (Demiryurek et al. 2009, Esri 2012, George et al. 2007). Another
method is the use of historical traffic profiles often referred to as speed profiles that are
used to produce travel time estimates (Nannicini 2009, Park et al. 2005, TomTom 2012).
Historical traffic profiles can represent the value of travel time observed at the time
intervals of each link for a specific period of time in the past (Kim et al. 2007). The use
of traffic profiles can be useful because it is not realistic to have a road network
completely covered by traffic recorders, and they can reduce computation time and
database storage and improve data quality (Chien and Kuchipudi 2003, Shaw 2000). A
historical profile can be considered summary statistics such as mean/median travel time
for each time slice (e.g., 60 minutes) of a road segment which are observed for certain
past time periods (e.g., 30 days). For instance, if mean travel time is used as a historical
profile, it represents the average value of the observed edge travel times over certain past
time periods (Park et al. 2005).
14
Kim et al. (2005) examined the value of real-time traffic information such as
accidents, bad weather, traffic congestion, etc., to optimize vehicle routing in a dynamic
network. Real-time traffic information combined with historical traffic data can be used
to develop routing strategies that tend to improve both cost and service productivity
measures. According to Kok et al. (2012) and Panahi and Delavar (2009), historical
traffic data can realistically represent peak-hour traffic congestion and help emergency
vehicles avoid these congested areas and improve travel time.
15
Chapter 3: Conceptual Framework and Methodology
The scope of this research was to find out if time-varying travel times derived
from historical traffic data applied to road network edges would affect the response times
and routes of emergency vehicles within the study area. The use of distance and free-
flow travel time as cost attributes is common in static networks but may not reflect or be
sufficient to estimate travel time for emergency vehicle routing, since they do not reflect
dynamically changing traffic conditions (Panahi and Delavar 2009).
The overall approach and objective of this study were segmented into four parts
or elements for better understanding. The first part was to successfully develop a
functioning dynamic road network for the study area. Analyses without a well-built
functioning road network would be difficult to undertake. The second part was to
successfully convert historical traffic volume into time-varying travel time profiles that
would represent realistic travel times for different times of the day and for each day of the
week. This is in contrast to traditional methods for estimating travel times that are the
same, regardless of the time and day (TomTom 2012). The third part was to effectively
incorporate these historical traffic profiles to road edges that are applied in realistic
emergency response scenarios. The fourth part was to compare travel-time costs derived
from historical traffic data to cost attributes based on distance and free-flow travel time.
This can provide a good estimation of the performance of different congestion avoidance
strategies in a realistic setting (Kok et al. 2012, Panahi and Delavar 2009). This chapter
discusses the technical aspects of the research including an explanation of the data
16
sources and how the data was acquired, prepared and used. Figure 4 shows the general
methodology used for this research.
Build
Road ND
Road Feature
Class
Clip Roads to
Study Area
EMS, Hospital & Incident Feature Classes
Start
Traffic Profile
Tables
Network
Dataset
Create Network
Dataset
Clip to
Study Area
Configure Traffic
Profile Tables
Road ND &
Junctions
Acquire State
Roads Feature
Class
Acquire EMS,
Hospital & Crash
Statistics
Acquire Historical
Traffic Data
Specify Attributes
and Assign
Evaluators
Create Route
Analysis Layer
Compare &
Analyze Results
Road Network
File GDBCreate GDB
Apply Analysis
Settings
Created by:
Michael
Winn
Perform Route
Analysis
Scenario 1
Perform Route
Analysis
Scenario 2
Locate Incident,
Response Unit &
Hospital
Figure 4. Methodology flow chart
17
3.1 Data Sources
Most of the geographic datasets used for this research were obtained from the
Utah Automated Geographic Reference Center (Utah AGRC). Datasets from the Utah
AGRC included road and highway system centerline data, emergency response facilities
or units and hospital/trauma center locations. Additional data comprised state, county,
and municipal boundaries and other information to create the base maps used for this
study.
Incident data was obtained from UDOT. In accordance with the Government
Records Access Management Act (GRAMA), it was necessary to obtain written
permission to obtain this data and was received electronically (Jones 2013). Incident data
was from actual 2010 vehicle crash site locations within Davis County and included
statistical data about the crashes.
Historical traffic data was acquired from the UDOT website. Historical traffic
profile tables were available from Esri. All data was considered public domain and was
available for use at no cost. The spatial reference for all data except HAFB was UTM
Zone 12N NAD83. HAFB spatial reference was UTM Zone 12N WGS84.
18
3.2 Data Preparation
The road centerline data was obtained from the Utah AGRC. The Utah AGRC
created a functional road network called the Street Network Analysis dataset. This
dataset contained many attribute fields, some of which were not used while other fields
were added or modified to incorporate historical traffic and other functionalities.
Although the Utah AGRC continues to improve and maintain the routing capability and
connectivity of its road network centerline features, it was discovered at the beginning of
this research that additional work was needed to prepare the road network for analysis.
Edge directionality and connectivity were issues that needed to be addressed and fixed
for the network to function properly. Connectivity and directionality cannot be over-
emphasized and will be discussed in more detail in subsequent sections (Granberg 2011,
Utah AGRC 2012).
3.2.1 Road Network Centerlines
The road network centerline data was extracted from the statewide road dataset by
clipping to a polygon feature that encompassed the urbanized areas of Weber and Davis
counties. This area feature closely resembles the boundary represented in the Ogden-
Layton Urbanized Area Functional Class System map (UDOT 2012). The road network
used for this study actually covers the urbanized areas of both Davis and Weber counties.
It was necessary to extend the network into Weber County to accommodate travel to the
two Level II trauma centers situated in the Ogden area (Figure 3). The Level I hospitals
located in northern Salt Lake County are outside the scope of this study and the road
network ends at the southern border of Davis County.
19
3.2.2 Road Classifications
To include historical traffic data for this study, the classification of road segments
had to be accomplished. All road segments were classified and coded based on UDOT’s
Urban Area Functional Classification system (Figure 5). Adherence to this classification
system was closely followed except for a few modifications necessary to fit the study.
These modifications were made by disaggregating the Urban Principal Arterial
classification into several different categories (e.g., ramps and other freeways) and
aggregating urban local roads into the Urban Minor Collector classification (Federal
Highway Administration [FHWA] 1989, Nichol 2010, UDOT 2001; 2012). Table 1
shows a list of the functional classifications, their definitions and the number of road
segments associated with each classification. Functional classification (FC) codes 3, 5,
and 10 were aggregated under FC codes 11, 12, and 14, respectively.
20
Table 1. Urban Area Functional Classification system
Figure 5. Road network and the Urban Area Functional Classification system
FC Code Functional Classification Basic Functional Classification Definition Road Segments
3 Urban Principal Arterial - Interstate - Ramp Ramp feature (see FC Code 11) 192
5 Urban Principal Arterial - Other Freeways - Ramp Ramp feature (see FC Code 12) 20
10 Urban Principal Arterial - Other - Ramp Ramp feature (see FC Code 14) 42
11 Urban Principal Arterial - Interstate Interstates (e.g., I-15) 212
12 Urban Principal Arterial - Other Freeways Other Freeways (e.g., SR 67 Legacy Highway) 13
14 Urban Principal Arterial - OtherServes major activity centers. Majority of trips and
through traffic.330
16 Urban Minor ArterialTrips of moderate length, lower mobility than
primary arterials. 1,299
17 Urban Collector
Land access and circulation within and into
residential neighborhoods, commercial and
industrial areas. Collects from local streets and
channels to arterial system.
1,567
19 Urban Minor Collector
All routes not otherwise classified as
primary/principal arterials, minor arterials, or
collectors (e.g., urban local streets and roads).
24,297
27,972
21
3.2.3 Historical Hourly Traffic Volume Data
Traffic volume data is commonly referred to as traffic count or historical traffic
count data. It is considered historical because it is not real time data. This data
represents the number of vehicles passing a specific point or section of roadway for each
60 minute interval during a 24 hour period (UDOT 2010).
There are ninety-three Automatic Traffic Recorder (ATR) sites situated
throughout the state of Utah (UDOT 2010). Nine of these sites were used to collect
hourly traffic volume data for April 2010. April was preferred because it was thought it
might best represent typical traffic congestion in the study area. Weather conditions are
improving and normal workday traffic patterns are not interrupted by severe winter
weather conditions. School is in session and traffic patterns due to summer vacations,
furloughs or school recess are not affecting regular traffic patterns.
Of these nine ATR sites, five were chosen and matched to the Urban Area
Functional Classification system explained in Section 3.2.2. These ATR sites are
highlighted in Table 2 (0315, 0624, 0316, 0510, and 0601) with their associated
functional classification codes and location descriptions. In Table 2, four ATR sites
(0307, 0312, 0320 and 0609) were matched to rural area functional classifications (FC
Codes 1, 2, 6, and 7); however, no profiles were created because none of the road
segments were classified as rural. No ATR was found to represent FC Code 19. All nine
ATR sites are shown in Figure 6.
22
Table 2. ATR sites associated with the Functional Classification system
Figure 6. Geographic locations of the ATR sites
FC Code Functional Classification ATR Site Names Location County
1 Rural Principal Arterial - Interstate 0307 I 84 0.5 mile E of Mountain Green Int. MP 92.593 Morgan
2 Rural Principal Arterial - Other 0312 SR 6 4.5 miles SE of SR 89, Moark Jct. MP 182.390 Utah
3 Urban Principal Arterial - Interstate - Ramp 0315 Same as FC 11
5 Urban Principal Arterial - Other Freeways - Ramp 0624 Same as FC 12
6 Rural Minor Arterial 0320 SR 39 0.5 mile W of SR 158, Ogden Cyn. MP 13.243 Weber
7 Rural Major Collector 0609 SR 167 1.2 miles W of Mountain Green Int. MP 1.250 Morgan
10 Urban Principal Arterial - Other - Ramp 0316 Same as FC 14
11 Urban Principal Arterial - Interstate 0315 I 15 1.8 miles S of Lagoon Drive Int. MP 321.545 Davis
12 Urban Principal Arterial - Other Freeways 0624 SR 67 Legacy Highway MP 0.944 Davis
14 Urban Principal Arterial - Other 0316 SR 89 2 miles S of SR 193, Hillfield Road, Layton MP 402.695 Davis
16 Urban Minor Arterial 0510 SR 218 100 N 319 W, Smithfield MP 7.700 Cache
17 Urban Collector 0601 SR 92 American Fork Canyon W Toll Booth MP 7.873 Utah
19 Urban Minor Collector NA Represents all unclassified and 'Local Roads'
23
3.2.4 Grouping Historical Traffic Volume Data
The April 2010 hourly traffic volume data was grouped by weekdays and
weekends and averaged (Park et al. 2005). Weekday means Monday thru Friday, a total
of twenty-two days. Weekend means Saturday and Sunday, four days for each, a total of
8 days. There were 30 days total in April. Tables 3 and 4 show hourly traffic counts for
ATR site 0316. In Table 3, the hours are displayed along the top row and weekends are
highlighted.
Table 3. April 2010 traffic volumes for ATR site 0316
ATR Date 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0316 4/1/2010 238 116 82 97 221 670 1479 2333 2560 1766 1609 1670 1668 1776 1886 2401 2914 3379 2281 1424 1328 1119 744 363
0316 4/2/2010 233 129 67 94 180 499 1252 2117 2227 1783 1767 1888 1880 2011 2187 2492 2964 3130 2320 1594 1266 1151 933 638
0316 4/3/2010 400 239 144 102 118 197 432 673 1108 1144 1298 1478 1864 1713 1747 1719 1955 1970 1276 1020 1217 1252 906 538
0316 4/4/2010 333 213 142 89 95 152 278 397 597 818 1066 1169 1743 1619 1514 1639 2166 1790 1631 1729 1711 1184 676 306
0316 4/5/2010 183 104 73 101 201 658 1448 2252 2231 1686 1614 1674 1652 1755 1778 2127 2677 3181 1974 1270 859 686 520 289
0316 4/6/2010 193 114 87 88 194 627 1323 2044 2150 1576 1383 1538 1565 1523 1618 2002 2600 3009 2081 1288 1017 864 812 371
0316 4/7/2010 187 109 73 87 181 720 1447 2371 2403 1729 1543 1622 1737 1798 1820 2310 2853 3308 2264 1437 1208 1013 669 360
0316 4/8/2010 191 131 91 109 191 702 1438 2400 2420 1779 1669 1711 1760 1764 1918 2349 2948 3445 2404 1390 1252 1055 704 344
0316 4/9/2010 233 141 84 107 193 612 1301 2252 2133 1748 1706 1811 1865 1869 2057 2465 2960 3228 2158 1487 1186 1095 888 558
0316 4/10/2010 391 225 149 114 112 275 498 957 1263 1530 1512 1892 1886 2031 2003 2031 2239 2063 1845 1469 1251 1083 974 578
0316 4/11/2010 339 239 137 102 91 152 303 377 637 693 1034 989 1365 1216 1341 1400 1668 1553 1426 1295 1170 919 514 250
0316 4/12/2010 144 95 69 84 185 670 1612 2666 2459 1734 1655 1591 1647 1594 1909 2442 2885 3278 2266 1386 1088 893 521 264
0316 4/13/2010 184 106 65 95 191 722 1630 2508 2538 1755 1566 1562 1659 1733 1890 2512 2844 3413 2307 1457 1168 919 621 307
0316 4/14/2010 192 104 73 84 196 688 1682 2692 2524 1837 1643 1728 1727 1811 2092 2605 3090 3536 2523 1665 1295 940 621 588
0316 4/15/2010 237 122 87 112 190 705 1597 2589 2591 2028 1709 1869 1800 1852 2080 2752 3346 3707 2526 1614 1445 1103 733 342
0316 4/16/2010 227 128 86 95 182 592 1417 2393 2381 1944 1740 1881 1839 2226 2798 3287 3559 3626 2459 1551 1301 1208 881 620
0316 4/17/2010 352 227 122 102 138 354 524 988 1588 1716 1858 2035 2093 2216 2123 2132 2527 2126 1928 1583 1323 1183 880 671
0316 4/18/2010 391 269 128 109 85 147 317 445 765 740 1036 1076 1403 1399 1472 1547 1950 1632 1463 1459 1451 995 566 305
0316 4/19/2010 142 107 60 95 185 674 1620 2631 2534 1844 1550 1705 1794 1729 2022 2535 3013 3396 2406 1413 1276 875 511 273
0316 4/20/2010 196 115 62 104 171 706 1656 2576 2467 1816 1741 1722 1743 1908 2034 2619 3303 3490 2526 1521 1262 1011 602 302
0316 4/21/2010 177 122 81 89 199 721 1721 2468 2391 1759 1566 1629 1703 1783 2065 2563 2896 3314 2324 1433 1141 962 562 317
0316 4/22/2010 173 117 79 108 199 677 1530 2358 2288 1658 1526 1592 1652 1739 1943 2467 3126 3360 2421 1643 1236 1080 701 506
0316 4/23/2010 224 131 110 102 181 581 1464 2592 2104 1926 1667 1823 1891 2150 2393 2481 3216 3703 2506 1661 1384 1065 916 810
0316 4/24/2010 588 246 128 103 118 258 606 1059 1586 1827 1806 1928 2004 2129 2138 2158 2580 2326 1931 1500 1307 1294 988 589
0316 4/25/2010 433 256 204 101 99 178 322 460 909 1004 1095 1099 1497 1303 1435 1735 2138 1746 1688 1358 1205 962 607 662
0316 4/26/2010 190 83 77 103 213 687 1642 2460 2291 1608 1475 1546 1610 1620 1971 2413 2951 3426 2453 1409 1103 937 550 291
0316 4/27/2010 178 95 70 92 195 710 1658 2465 2344 1779 1599 1721 1690 1695 1881 2658 3024 3298 2415 1375 1128 972 600 314
0316 4/28/2010 193 118 86 94 197 714 1591 2271 2259 1601 1370 1551 1524 1631 1825 2409 2770 3275 2296 1425 1226 847 538 344
0316 4/29/2010 207 148 84 94 178 685 1540 2334 2189 1621 1413 1568 1602 1564 1910 2519 2841 3316 2118 1414 1068 1029 692 342
0316 4/30/2010 197 140 89 107 179 573 1339 2095 2135 1677 1702 1599 1682 1901 2097 2436 3029 3330 2410 1574 1163 1108 847 846
24
Table 4 shows the hourly traffic counts grouped by weekdays and the weekend.
Averages were calculated for each hourly time slice throughout the day. Because Sunday
and Tuesday will be analyzed for all routing examples and scenarios, the highlighted
rows represent the four Tuesdays in April. Tuesday averages are shown below the 22-
day average to show a comparison between aggregated 22-day weekday averages and the
4-day Tuesday averages. Examples of tables and profiles associated with ATR and
traffic volume data was limited to ATR site 0316 to conserve space.
Table 4. April 2010 traffic volumes for ATR Site 0316, grouped
Weekday (M-F) 22-Day and Tuesday 4-Day Average
ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0316 4/1/2010 238 116 82 97 221 670 1479 2333 2560 1766 1609 1670 1668 1776 1886 2401 2914 3379 2281 1424 1328 1119 744 363
0316 4/2/2010 233 129 67 94 180 499 1252 2117 2227 1783 1767 1888 1880 2011 2187 2492 2964 3130 2320 1594 1266 1151 933 638
0316 4/5/2010 183 104 73 101 201 658 1448 2252 2231 1686 1614 1674 1652 1755 1778 2127 2677 3181 1974 1270 859 686 520 289
0316 4/6/2010 193 114 87 88 194 627 1323 2044 2150 1576 1383 1538 1565 1523 1618 2002 2600 3009 2081 1288 1017 864 812 371
0316 4/7/2010 187 109 73 87 181 720 1447 2371 2403 1729 1543 1622 1737 1798 1820 2310 2853 3308 2264 1437 1208 1013 669 360
0316 4/8/2010 191 131 91 109 191 702 1438 2400 2420 1779 1669 1711 1760 1764 1918 2349 2948 3445 2404 1390 1252 1055 704 344
0316 4/9/2010 233 141 84 107 193 612 1301 2252 2133 1748 1706 1811 1865 1869 2057 2465 2960 3228 2158 1487 1186 1095 888 558
0316 4/12/2010 144 95 69 84 185 670 1612 2666 2459 1734 1655 1591 1647 1594 1909 2442 2885 3278 2266 1386 1088 893 521 264
0316 4/13/2010 184 106 65 95 191 722 1630 2508 2538 1755 1566 1562 1659 1733 1890 2512 2844 3413 2307 1457 1168 919 621 307
0316 4/14/2010 192 104 73 84 196 688 1682 2692 2524 1837 1643 1728 1727 1811 2092 2605 3090 3536 2523 1665 1295 940 621 588
0316 4/15/2010 237 122 87 112 190 705 1597 2589 2591 2028 1709 1869 1800 1852 2080 2752 3346 3707 2526 1614 1445 1103 733 342
0316 4/16/2010 227 128 86 95 182 592 1417 2393 2381 1944 1740 1881 1839 2226 2798 3287 3559 3626 2459 1551 1301 1208 881 620
0316 4/19/2010 142 107 60 95 185 674 1620 2631 2534 1844 1550 1705 1794 1729 2022 2535 3013 3396 2406 1413 1276 875 511 273
0316 4/20/2010 196 115 62 104 171 706 1656 2576 2467 1816 1741 1722 1743 1908 2034 2619 3303 3490 2526 1521 1262 1011 602 302
0316 4/21/2010 177 122 81 89 199 721 1721 2468 2391 1759 1566 1629 1703 1783 2065 2563 2896 3314 2324 1433 1141 962 562 317
0316 4/22/2010 173 117 79 108 199 677 1530 2358 2288 1658 1526 1592 1652 1739 1943 2467 3126 3360 2421 1643 1236 1080 701 506
0316 4/23/2010 224 131 110 102 181 581 1464 2592 2104 1926 1667 1823 1891 2150 2393 2481 3216 3703 2506 1661 1384 1065 916 810
0316 4/26/2010 190 83 77 103 213 687 1642 2460 2291 1608 1475 1546 1610 1620 1971 2413 2951 3426 2453 1409 1103 937 550 291
0316 4/27/2010 178 95 70 92 195 710 1658 2465 2344 1779 1599 1721 1690 1695 1881 2658 3024 3298 2415 1375 1128 972 600 314
0316 4/28/2010 193 118 86 94 197 714 1591 2271 2259 1601 1370 1551 1524 1631 1825 2409 2770 3275 2296 1425 1226 847 538 344
0316 4/29/2010 207 148 84 94 178 685 1540 2334 2189 1621 1413 1568 1602 1564 1910 2519 2841 3316 2118 1414 1068 1029 692 342
0316 4/30/2010 197 140 89 107 179 573 1339 2095 2135 1677 1702 1599 1682 1901 2097 2436 3029 3330 2410 1574 1163 1108 847 846
22-Day Avg 196 117 79 97 191 663 1518 2403 2346 1757 1601 1682 1713 1792 2008 2493 2991 3370 2338 1474 1200 997 689 427
TU 4-Day Avg 188 108 71 95 188 691 1567 2398 2375 1732 1572 1636 1664 1715 1856 2448 2943 3303 2332 1410 1144 942 659 324
Saturday Average (4 days)
ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0316 4/3/2010 400 239 144 102 118 197 432 673 1108 1144 1298 1478 1864 1713 1747 1719 1955 1970 1276 1020 1217 1252 906 538
0316 4/10/2010 391 225 149 114 112 275 498 957 1263 1530 1512 1892 1886 2031 2003 2031 2239 2063 1845 1469 1251 1083 974 578
0316 4/17/2010 352 227 122 102 138 354 524 988 1588 1716 1858 2035 2093 2216 2123 2132 2527 2126 1928 1583 1323 1183 880 671
0316 4/24/2010 588 246 128 103 118 258 606 1059 1586 1827 1806 1928 2004 2129 2138 2158 2580 2326 1931 1500 1307 1294 988 589
4-Day Avg 433 234 136 105 122 271 515 919 1386 1554 1619 1833 1962 2022 2003 2010 2325 2121 1745 1393 1275 1203 937 594
Sunday Average (4 days)
ATR Date 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0316 4/4/2010 333 213 142 89 95 152 278 397 597 818 1066 1169 1743 1619 1514 1639 2166 1790 1631 1729 1711 1184 676 306
0316 4/11/2010 339 239 137 102 91 152 303 377 637 693 1034 989 1365 1216 1341 1400 1668 1553 1426 1295 1170 919 514 250
0316 4/18/2010 391 269 128 109 85 147 317 445 765 740 1036 1076 1403 1399 1472 1547 1950 1632 1463 1459 1451 995 566 305
0316 4/25/2010 433 256 204 101 99 178 322 460 909 1004 1095 1099 1497 1303 1435 1735 2138 1746 1688 1358 1205 962 607 662
4-Day Avg 374 244 153 100 93 157 305 420 727 814 1058 1083 1502 1384 1441 1580 1981 1680 1552 1460 1384 1015 591 381
25
3.2.5 Historical Traffic Volume Profiles
The historical traffic volume profiles were created based on the weekday and
weekend averages for the five ATR sites explained in Section 3.2.3 and displayed in
Table 2. Three profiles related to ATR site 0316 are shown in Figures 7, 8, and 9.
Figure 7 represents the profile from the 4-day Tuesday averages found in Table 4. For
comparison, the red dashed line in Figure 7 represents the 22-day weekday traffic count
averages. There is little noticeable difference between the profiles. Figure 8 and Figure
9 illustrate the profile from the 4-day weekend (Saturday and Sunday, respectively)
traffic count averages.
Esri has provided a free-flow traffic profiles table for simulating time-dependent
traffic condition (Esri 2012). There were 98 records with 5 minutes intervals in the
profiles table originally created for San Francisco areas (Esri 2012). Each record has a
unique identifier or number and stores the free-flow scale factor for each time interval.
However, in dynamic network analysis, the shorter the time interval is, the more
computational power required to run a dynamic network analysis. Therefore, to reduce
the computation complexity and to accommodate UDOT traffic volume data, this study
converted the Esri 5-minutes free-flow traffic profiles into hourly free-flow traffic
profiles and created the ‘DailyProfiles_Time_60min’ table (shown in Table 5). The table
stores the free-flow scale factors or multipliers for each 60 minute time interval or time
slice during a 24 hour day. This is 24 equal time intervals represented by 24 fields. The
profile numbers are listed in the ‘ProfileID’ field. Because of the number of fields in the
‘DailyProfiles_Time_60min’ table, the field names were shortened and some fields were
omitted.
26
Figure 7. ATR site 0316 traffic volume profile - Tuesday average, April 2010
Figure 8. ATR site 0316 traffic volume profile – Saturday average, April 2010
Figure 9. ATR site 0316 traffic volume profile – Sunday average, April 2010
27
The traffic volume profiles in this study (created based on the weekday and
weekend averages for the five ATR sites) were visually matched to the free-flow traffic
profiles created from the ‘DailyProfiles_Time_60min’ table (Table 5) by comparing the
profile or graph lines and choosing the profile with the best fit. It should be noted that
the method of visually comparing profiles is subjective and can introduce bias. Of the 98
free-flow traffic profiles found in the ‘DailyProfiles_Time_60min’ table (Table 5), there
are nine free-flow traffic profiles (‘ProfileID’ 3, 8, 12, 14, 21, 91, 92, 96, and 98) as
shown in Figures 10 through 18, respectively, matched to the traffic volume profiles
created from ATR sites 0315, 0624, 0316, 0510, and 0601 (Table 2). The three free-flow
traffic profiles that matched closest to the traffic volume profiles associated with ATR
site 0316 shown in Figures 7, 8 and 9 were profiles 91, 14 and 3. These free-flow traffic
profiles can be viewed in Figures 15, 13 and 10, respectively.
Table 6 shows how the nine free-flow traffic profiles (shown in Figures 10
through 18) are arranged and correspond to the nine road functional classifications and
the days of the week. The nine ‘ProfileID’ free-flow traffic profile numbers are
organized and stored in the ‘Project_Profiles’ table (Table 7) and correspond to the daily
traffic pattern of each road segment. The fields, ‘Profile_1' through ‘Profile_7’, in the
‘Project_Profiles’ table are populated with ‘ProfileID’ numbers and match to the same
profile numbers found in the ‘DailyProfiles_Time_60min’ table. The ‘Profile_1’ field
shows the ‘ProfileID’ of Sunday free-flow traffic profile; ‘Profile_7 field represents the
‘ProfileID’ of Saturday free-flow traffic profile; ‘Profile_2’ through ‘Profile_6’ fields are
for Monday through Friday. Therefore, there is a ‘ProfileID’ for each day of the week for
28
all 27, 972 road segments or records. Table 8 represents the ‘ProjectArea’ feature class.
Each record represents a road segment.
Table 5. ‘DailyProfiles_Time_60min’ file geodatabase table
Table 6. Profile IDs from the ‘DailyProfiles_Time_60min’ table
FC Code Functional Class SUN MON TUE WED THR FRI SAT Notes
3 Urban Principal Arterial - Interstate - Ramp 8 98 98 98 98 98 92 Same as FC Code 11
5 Urban Principal Arterial - Other Freeways - Ramp 12 91 91 91 91 91 12 Same as FC Code 12
10 Urban Principal Arterial - Other - Ramp 3 91 91 91 91 91 14 Same as FC Code 14
11 Urban Principal Arterial - Interstate 8 98 98 98 98 98 92
12 Urban Principal Arterial - Other Freeways 12 91 91 91 91 91 12
14 Urban Principal Arterial - Other 3 91 91 91 91 91 14
16 Urban Minor Arterial 96 21 21 21 21 21 8
17 Urban Collector 12 3 3 3 3 3 3
19 Urban Minor Collector 8 98 98 98 98 98 92
29
Figure 10. ‘DailyProfiles_Time_60min’ table: Profile 3
Figure 11. ‘DailyProfiles_Time_60min’ table: Profile 8
Figure 12. ‘DailyProfiles_Time_60min’ table: Profile 12
30
Figure 13. ‘DailyProfiles_Time_60min’ table: Profile 14
Figure 14. ‘DailyProfiles_Time_60min’ table: Profile 21
Figure 15. ‘DailyProfiles_Time_60min’ table: Profile 91
31
Figure 16. ‘DailyProfiles_Time_60min’ table: Profile 92
Figure 17. ‘DailyProfiles_Time_60min’ table: Profile 96
Figure 18. ‘DailyProfiles_Time_60min’ table: Profile 98
32
Table 7. 'Project_Profiles' file geodatabase table
Table 8. 'ProjectArea' feature class attribute table
OBJECTID LENGTH_MI FC_CODE EdgeFCID EdgeFID FreeFlowMi Profile_1 Profile_2 Profile_3 Profile_4 Profile_5 Profile_6 Profile_7
1 0.051704 16 53 1 0.077556 96 21 21 21 21 21 8
17 0.063222 14 53 17 0.094832 3 91 91 91 91 91 14
18 0.329915 11 53 18 0.304537 8 98 98 98 98 98 92
20 0.079990 17 53 20 0.119984 12 3 3 3 3 3 3
23 0.035119 19 53 23 0.052679 8 98 98 98 98 98 92
51 0.045459 3 53 51 0.109103 8 98 98 98 98 98 92
3493 0.107606 10 53 3493 0.161408 3 91 91 91 91 91 14
14446 0.230291 5 53 14446 0.345437 12 91 91 91 91 91 12
14449 0.453260 12 53 14449 0.494466 12 91 91 91 91 91 12
27972 27972
13 of 21 total fields
Field Name Data Type 27, 972 total records
OBJECTID Object ID
LENGTH_MI Double
FC_CODE Short Integer
FUNCTIONAL_CLASS Text
Shape_Length Double
EdgeFCID Long Integer
EdgeFID Long Integer
EdgeFrmPos Double
EdgeToPos Double
FreeFlowMi Double
Profile_1 Long Integer
Profile_2 Long Integer
Profile_3 Long Integer
Profile_4 Long Integer
Profile_5 Long Integer
Profile_6 Long Integer
Profile_7 Long Integer
Val_Dir Short Integer
SPFREEFLOW Short Integer
SPWEEKDAY Short Integer
SPWEEKEND Short Integer 21 of 21 total fields
OBJECTID SPD_LMT ONE_WAY MINUTES LENGTH_MI FC_CODE FT_Min TF_Min OneWay Shape_Len
1 40 0 0.077556 0.051704 16 0.077556 0.077556 83.209915
2 40 0 0.097135 0.064757 16 0.097135 0.097135 104.216068
3 40 0 0.061795 0.041197 16 0.061795 0.061795 66.300000
4 40 1 0.064878 0.043252 16 0.064878 0.064878 FT 69.607615
5 40 0 0.031018 0.020678 16 0.031018 0.031018 33.278655
27968 40 0 0.097976 0.065317 16 0.097976 0.097976 105.118241
27969 40 0 0.099292 0.066195 16 0.099292 0.099292 106.530051
27970 40 0 0.021112 0.014075 16 0.021112 0.021112 22.651280
27971 55 0 0.141260 0.129488 14 0.141260 0.141260 208.391454
27972 40 0 0.070704 0.047136 16 0.070704 0.070704 75.858100
10 of 85 total fields
Field Name Data Type 27972 total records
OBJECTID Object ID
LABEL Text
SPD_LMT Short Integer
ONE_WAY Short Integer
MINUTES Double
LENGTH_MI Double
FC_CODE Short Integer
FUNCTIONAL_CLASS Text
FT_Minutes Double
TF_Minutes Double
FT_WeekdayMinutes Double
TF_WeekdayMinutes Double
FT_WeekendMinutes Double
TF_WeekendMinutes Double
OneWay Text
Shape_Length Double 16 of 85 total fields
33
3.2.6 Modeling Historical Traffic Data
Historical traffic data is at the heart of this research and is essential for creating a
dynamic road network that will represent peak-hour traffic congestion and assist first
responders to avoid these congested areas and improve travel time. The approach to
modeling historical data for this study has its origins in the private sector by industry
leaders who provide navigation products and location-based services (LBS) to the general
public and other vendors and partners (Esri 2013a, Tele Atlas 2009, TomTom 2012).
Instead of storing historical traffic data for each individual road segment, related tables
are used to store and represent the changes in travel time throughout the day (Esri 2012).
Two tables work in conjunction with the ‘ProjectArea’ feature class that stores the road
segment features (Table 8). These are the ‘DailyProfiles_Time_60min’ and
‘Project_Profiles’ tables that are discussed in Section 3.2.5 and represented in Tables 5
and 7, respectively.
Each road segment in the ‘ProjectArea’ feature class has a unique identifier. Each
record in the ‘DailyProfiles_Time_60min’ table where the free-flow multipliers are
stored, also has a unique identifier or ‘ProfileID’ for each record or traffic profile. The
‘Project_Profiles’ table stores the free-flow travel time and the ‘ProfileID’ that best
represents traffic for each day of the week and for each road segment. This table joins
the road segments in the ‘ProjectArea’ feature class to the various traffic profiles in the
‘DailyProfiles_Time_60min’ table through a unique identifier found in the ‘EdgeFID’
field that correlates to the ‘ObjectID’ field in the ‘ProjectArea’ feature class (Esri 2012).
Other values are stored in ‘ProjectArea’ and will be discussed in the following sections.
34
All these network sources are required for historical traffic data to work in the
network dataset. When a road segment in the ‘ProjectArea’ feature class is related to a
traffic profile in the ‘DailyProfiles_Time_60min’ table by the ‘Project_Profiles’ join
table, the travel time for any 60 minute time slice on a given day is calculated. This
calculation is based on the free-flow travel time value stored in the ‘Project_Profiles’
table and the free-flow multiplier value associated with the ‘ProfileID’ in the
‘DailyProfiles_Time_60min’ table.
Example: If a road segment with an ‘ObjectID’ of 20 in the ‘ProjectArea’ feature class
(not shown in Table 8) is related to a record in the ‘Project_Profiles’ table with an
‘EdgeFID’ of 20 (Table 7) and has a ‘ProfileID’ value of 3 for Tuesday, the free-flow
travel time (‘FreeFlowMi’) in minutes is 0.119984. The expected travel time at 1800
(Figure 10) for Profile 3 will be calculated by multiplying the road segment free-flow
travel time (0.119984) by the profile's free-flow multiplier or time factor value of
1.051520 (see Table 5 at 1800 for ‘ProfileID’ 3).
35
3.2.7 Incorporating Historical Traffic Data
After the historical traffic tables were configured and populated correctly, they
were incorporated into the network dataset. This is completed during the network
creation but prior to the building process. Figure 19 shows the properties associated with
the historical traffic tables and how the ‘DailyProfiles_Time_60min’ and
‘Project_Profiles’ join tables are configured. Note that the ‘First Time Slice’ is set to
4:00 am and the ‘Last Time Slice’ is set to 10:00 pm because the free-flow multiplier
value from 10:00 pm to 4:00 am is 1.
The location where network cost attributes are applied to road network edges is
shown in Figure 20. The distance cost is displayed as ‘Length’ and corresponds to the
‘LENGTH_MI’ field in the ‘Project_Profiles’ table in Table 7 and the ‘ProjectArea’
feature class in Table 8. The free-flow travel time cost is displayed as ‘MINUTES’ and
corresponds to the 'FreeFlowMi’ field in the ‘Project_Profiles’ table in Table 7 and to the
‘MINUTES’ field in the ‘ProjectArea’ feature class in Table 8. The time-varying travel
time cost is a calculated value based on historical traffic data and is displayed as
‘TravelTime’ in Figure 20. Other costs and descriptors shown in Figure 20 were
assigned values but are not used in this analysis. ‘Oneway’ restrictions will be explained
in Section 3.3.1. Global turns will be explained in Section 3.3.2.
36
Figure 19. Network Dataset properties associated with the historical traffic tables
Figure 20. Assignment of network attributes
37
3.3 Developing the Road Network Model
Esri ArcGIS Network Analyst was used to create a dynamic road network model
and spatio-temporal database for incorporating historical traffic data and performing the
shortest path analysis. The road network model is considered dynamic in the sense that
cost attributes such as travel time change with respect to time. The database is
considered spatio-temporal in the sense it has spatial, non-spatial and temporal
characteristics such as location, attribute and time (Shaw 2000). ArcGIS is suitable for
this kind of research because it is commercially available and the Network Analyst
extension is included in the student edition of ArcGIS. Network Analyst provides the
functionality to incorporate historical traffic data and model the time-dependent costs of
traveling the network.
The term Network Dataset (ND) is important to the understanding of how a road
network is modeled and functions in Network Analyst. It is defined by Esri as a
collection of topologically connected network elements (e.g., edges, junctions, and turns)
that are derived from network sources (e.g. feature classes) and used to represent a road
network. Each network element is associated with a collection of network attributes
(e.g., cost, descriptor, hierarchy, and restriction). When any analysis is performed in
Network Analyst, it is performed on a network dataset (Esri 2013b). This term is used to
when describing road network features.
Several steps were required to create the road network dataset. The first step was
to create a file geodatabase (FGDB) as a repository for all network related elements and
feature classes including the traffic profile tables. The network dataset was created in a
feature dataset to maintain topology and spatial reference. In a geodatabase-based
38
network dataset, all feature classes participating as sources in a network are stored in a
feature dataset (Esri 2013b). Figure 21 shows a view of the file geodatabase data model.
Although it is not necessary, a relationship class was created between the
‘ProjectArea’ feature class and the ‘Project_Profiles’ table. This made the process of
editing road network features faster and simpler to manage. The records and unique
identifiers in the ‘ProjectArea’ feature class and in the ‘Project_Profiles’ table should be
identical. The final step prior to performing the analysis was to build the network
dataset. Building the network dataset is the process of creating network elements,
establishing connectivity and assigning network values (Esri 2013c).
Figure 21. File geodatabase data model
39
3.3.1 One Way Restrictions
One Way restrictions are applied to limit travel on one way roads and avoid
routing irregularities. There are 184 miles of one way road segments in the road network
comprised mostly of highways and ramps. All road segments were digitized in the
‘from-to’ (FT) direction. If the ‘OneWay’ field in the ‘ProjectArea’ feature class was
populated with FT, it means travel was only allowed in the digitized direction of the road
segments. One Way restrictions can be set to ‘Prohibit’, ‘Avoid’, or ‘Prefer’ for one way
road segments (Esri 2013d). All one way roads segments are restricted and set to
‘Prohibit’. The ‘Prefer’ and ‘Avoid’ parameters were not used because they were
considered subjective and would bias the analysis.
Example: Figure 22 shows a route from Incident 1 to Ogden Regional Medical Center
with the One Way restriction on. The correct ramps and lanes were traveled for I-84.
Figure 23 shows the route from Incident 1 to Ogden Regional Medical Center with the
One Way restriction off. Notice the incorrect ramps and lanes for I-84 were traveled.
40
Figure 22. Correct one-way travel, from Incident 1 to Ogden Regional Medical Center
Figure 23. Incorrect one-way travel, from Incident 1 to Ogden Regional Medical Center
41
3.3.2 Global Turns
Global turn delays are used as a kind of cost attribute to improve travel time
estimates by delaying movements from one road segment to another. These delays are
also referred to as turn penalties. There are four types of turn directions used in the
study: straight, reverse, right and left turn. Global turn delays are not intended to be as
accurate as the turn feature class model of applying turn penalties (Esri 2013e). Global
turns were applied to the free-flow travel time and time-varying travel time cost
attributes. They are not available for use with the distance cost attribute.
If road hierarchies were applied, more turn directions would be available for use.
Because road hierarchies are not used, all roads are considered local roads and the
numbers of turn directions to choose from were reduced. This made the application of
turn delays simpler but less exact. The default Esri turn penalty values associated with
the turn directions and descriptions in Figure 24 were not considered suitable for this
study area. Averaging the default turn penalty seconds for each turn category shown in
Figure 24 produces a more representative turn penalty value for modeling emergency
response vehicle turn movements. Table 9 show the directions and penalties in seconds
used to model the turn delays. The applied values for each turn category were derived by
averaging the seconds shown in Figure 24.
Example: There are 4 left turns with the following default Esri values; 2, 10, 5 and 8
seconds. The average is 6 seconds. The default global turn delay values that are applied
in this study are shown in Figure 25. The calculated values are listed in Figure 26. The
default values for turn angles were used.
42
Table 9. Global turn delay directions and penalty values in seconds
Figure 24. Turn categories available for various road types
Direction Description Seconds (default) Seconds (applied)
Straight From Local to Local Road Across No Roads 0 0
Straight From Local to Local Road Across Local Road 2 4
Reverse From Local To Local Road 3 7
Right Turn From Local To Local Road 2 3
Left Turn From Local To Local Road 2 6
43
Figure 25. Global turn delay default settings
Figure 26. Global turn delay customized settings
44
In general, ambulance operators are allowed some privileges when responding to
an incident; however, safety is their number one priority. Operators are responsible for
the safe operation of the response vehicle at all times, including compliance with all
traffic laws. Usually emergency vehicles are prohibited from exceeding the posted speed
limit when approaching and crossing an intersection with the right-of-way, and they must
come to a complete stop before proceeding through a controlled intersection or using the
opposing traffic lanes to approach an intersection (International Association of Fire
Chiefs [IAFC] 2013, McDonald 2013).
In addition to safety concerns, vehicle size and maneuverability were taken into
account when assigning turn penalty values. Emergency response vehicles are larger and
more challenging to drive when negotiating turns than smaller vehicles. When making
turns or negotiating curves too fast, an ambulance could be susceptible to losing control
or even overturning due to its size and box shaped design. At a minimum, equipment,
patients, and medical personnel working with patients during transport could be tossed
about or injured. Caution with or without lights and sirens is important and will take a
few seconds longer when negotiating turns. Additional factors might include weather,
road conditions, and intersection sizes. Based on these policies and other factors
mentioned, averaging turn penalty second values is thought to be a reasonable attempt to
model emergency response routing more realistically (McDonald 2013).
45
Chapter 4: Analysis and Results
This analysis comprises two routing examples centered on two discrete vehicle
accident locations selected from 2010 UDOT crash data (shown in Table 10 as IN-1 and
IN-2). Each example comprises two scenarios. The first scenario, which will be referred
to as S1, represents an ambulance on an emergency call from a ground emergency
response unit (e.g., fire station) to the scene of a traffic incident (e.g., car crash). The
second scenario, which will be referred to as S2, represents an ambulance leaving the
scene of the accident transporting the victim(s) to the nearest hospital or trauma center.
Figure 27 shows an example routing solution for scenarios 1 and 2.
The ‘Closest Facility’ solver in Network Analyst was used to locate the nearest
ground emergency response unit and hospital to each incident. The ‘Route’ solver in
Network Analyst was used to find the shortest path between two locations using a
distance-based cost attribute, the fastest route using a time-based cost attribute known as
the free-flow travel time, and the optimal route using a time-varying cost attribute based
on historical traffic data.
For both routing scenarios, similarities and differences between route directions,
distances, and travel times generated from each cost attribute are compared and analyzed.
Emergency response routing based on cost attributes derived from historical travel-time
data and applied to network edges should assist emergency response vehicles to avoid
congested areas (Kok et al. 2012, Panahi and Delavar 2009). Figure 28 shows the
general process of the routing analysis for both routing scenarios in each example.
46
Table 10. Incident data from 2010 UDOT crash statistic
Figure 27. Example of routing scenarios S1 and S2
Incident Crash ID Junction Type Crash Severity Location
IN-1 10369590 4-Leg Intersection Non-Incapacitating Injury 2000W, at 1800 N
IN-2 10364031 4-Leg Intersection Non-Incapacitating Injury Boynton at Fairfield Rd
47
Network
Dataset
Identify Incident
Location
Compare &
Analyze
ResultsCreated by:
Michael
Winn
Locate Nearest
Ground Unit with
‘Closest Facility’
Solver
Locate Nearest
Hospital with
‘Closest Facility’
Solver
Nearest
Hospital
Nearest
Ground Unit
Solve shortest path
with ‘Route’ Solver
Run 1 (R1)
DIST
Run 2 (R2)
FFTT
Run 3 (R3)
TVTT
Apply Analysis
Settings
Apply Analysis
Settings
Apply Analysis
Settings
Scenario 1
(S1)
Scenario 2
(S2)
Compare &
Analyze
Results
Solve shortest path
with ‘Route’ Solver
Run 1 (R1)
DIST
Run 2 (R2)
FFTT
Run 3 (R3)
TVTT
Apply Analysis
Settings
Figure 28. Route analysis flowchart
48
For all routing examples (IN-1, IN-2), S1 and S2 are comprised of three routing
runs. The first run (R1) uses a distance cost attribute. The distance refers to the length in
miles of each road segment or edge in the network. This cost attribute or impedance will
be referred to as DIST.
The second run (R2) uses a travel time cost attribute. This travel time cost
represents a static shortest path calculation with no major impedances or cost other than
the base travel time for each road segment or edge. The base travel time is considered
fixed and proportional to the length of a road segment (Demiryurek et al. 2010). This
impedance is also known as the free-flow travel time or FFTT which is derived from the
free-flow speed. The FFTT speed is the speed a vehicle travels when it is not impeded by
other traffic movement. This is typically the posted speed limit but can be defined as five
miles per hour greater than the posted speed limit (Esri 2012, FHWA 2013). The
equation used to calculate the FFTT in minutes for each road segment is shown in
Equation 4.1.
Road Segment Length in Miles * (60 / Speed Limit in Miles per Hour)
Equation 4.1
The third run (R3) uses historical traffic data to model time-varying costs of
traveling on the network. Time-varying or time-dependent travel time costs are used to
find the best route from an origin to a destination. For this analysis, time-varying travel
time is referred to as TVTT. TVTT is what makes the road network considered dynamic.
How historical traffic data is modeled and incorporated into this analysis was explained
in Sections 3.2.6 and 3.2.7.
49
Sunday and Tuesday are analyzed for all routing examples and scenarios. The
start times for each routing scenario were run at the top of the hour (e.g., 0700, 0800,
etc.) for a 24 hour period. Sunday was selected to best represent weekend traffic and
Tuesday was selected to best represent weekday traffic. These selections were based on
grouping days by weekdays and weekends. Niemeier et al. (2002) claimed that “It is
well accepted that temporal profiles of daily traffic volumes tend to be similar across
certain days and time periods. For instance, the typical traffic pattern seen on Tuesday is
often very similar to the traffic pattern seen on Wednesday and Thursday. Saturday and
Sunday tend to have similar traffic patterns, whereas the patterns on Monday and Friday
are usually unique”. Some liberties were taken with these selections. Two days were
selected for analysis to reduce the size of the study.
As explained in Section 3.3.2, global turn delays are only available for use with
the FFTT and TVTT impedances. When executing the ‘Route’ solver in Network
Analyst, all three cost attributes (DIST, FFTT, TVTT) run and generate results, but only
the specified impedance is used to optimize the solution. For example, when utilizing
DIST as impedance, the ‘Route’ solver will produce the best route for the specified
impedance, which is the shortest distance route. The route run results will generate three
attribute fields. The ‘DIST (mi)’ field represents the distance or total length of the route
in miles. The ‘FFTT (min)’ field represents the free-flow travel time in decimal minutes
for the specified time interval of the route without the additional travel-time costs that
would normally be added when FFTT and TVTT impedances are used to optimize the
solution. This is because global turn restrictions are not available when the DIST
impedance is used. The ‘TVTT (min)’ field represents the time-varying travel time in
50
decimal minutes for the specified time interval of the route without the additional travel-
time costs for the same reasons as explained for the ‘FFTT (min)’ field.
When the ‘Closest Facility’ solver is used, no start or end time attribute fields are
generated. When the ‘Route’ solver is used, start and end time attribute fields are
generated when FFTT and TVTT impedances are applied. However, no start or end time
attribute fields are generated when the DIST impedance is used.
Figure 29 shows the analysis settings that are available for the ‘Closest Facility’
solver. Figure 30 shows the analysis settings that are available for the ‘Route’ solver.
When the DIST and FFTT impedances are applied, time settings were used but were not
necessary. These time settings are named ‘Use Time’ in ‘Closest Facility’ solver and
‘Use Start Time’ in ‘Route’ solver. For instance, if route runs were performed using the
DIST and FFTT cost attributes every hour for 24 hours, the distance and travel time
values would be the same. Changes only occur when using time setting and the TVTT
impedance. This is required in order to apply historical traffic data. Only the impedance
applied to the route run is used to optimize the solution. For instance, if the TVTT
attribute is used as the cost attribute, DIST and FFTT costs can still be accumulated and
reported to assist in the analysis but the path is actually calculated based on the TVTT
(Esri 2013f).
4.1 Route Example for IN-1
4.1.1 IN-1: Closest Facility Analysis
Incident 1 (IN-1) is located in Clinton at the intersection of 200W, at 1800N
(Table 10). After the incident location was identified, the ‘Closest Facility’ solver was
51
Figure 29. Analysis settings available for ‘Closest Facility’ solver
Figure 30. Analysis settings available for ‘Route’ solver
52
used to locate the nearest ground unit and hospital/trauma center. The analysis settings
for each cost attribute used to find the nearest ground unit are shown in Table 11. The
analysis settings for each cost attribute used to find the nearest hospital are shown in
Table 12. The only difference in the settings between Tables 11 and 12 is in the ‘Travel
From’ field. The ‘Facility to Incident’ setting was used to find the nearest ground unit to
IN-1, and the ‘Incident to Facility’ setting was used to find the nearest hospital from IN-
1.
The same methodology was used to determine the nearest ground unit and
hospital to IN-1. The DIST, FFTT and TVTT impedances were applied in both
instances. Although distance should determine the shortest route, it was believed that
using the FFTT and TVTT cost attributes would validate that the shortest routes were
also the routes with the least travel time. In other words, if two hospitals were close in
total distance from the same incident, TVTT could determine that during a time of heavy
traffic congestion, the travel time to the closer hospital could be greater than the travel
time to the farther hospital.
All route runs were run for Tuesday at 1700. After previously examining the
TVTT values for a 24 hour period of time, the 1700 to 1800 time slice proved to have the
greatest TVTT in both cases. Table 13 shows the results of runs applying DIST, FFTT
and TVTT impedances to determine the nearest ground unit to IN-1. Table 14 shows the
results of runs applying DIST, FFTT and TVTT impedances to determine nearest hospital
from IN-1. When observing route run results in Tables 13 and 14, the accumulated
values are shown in italicized red font and are for reference and comparison only. The
bolded values are the values based on the applied impedance. The same settings were
53
applied to all routing examples and scenarios. Changes in routes are shown in Tables 13
and 14 under the ‘Run/Route’ field, and the ‘Figure’ field indicates the corresponding
figure showing the route changes. The ‘Run/Route’ field is used to identify the route
runs. Figures 31 through 36 show the routes associated with the cost attribute used.
For Table 13, run routes R1/A, R2/A, and R3/A indicate the shortest, fastest and
optimal routes, respectively for finding the nearest ground unit to IN-1. For Table 14,
run routes R1/A, R2/B, and R3/C indicate the shortest, fastest and optimal routes,
respectively for finding the nearest hospital from IN-1. As a result of these run routes
and applying DIST, FFTT and TVTT as impedances, it was determined the closest
ground unit to IN-1 is Clinton Fire Department and the closest hospital from IN-1 is
Davis Hospital.
Table 11. Analysis settings for finding nearest ground unit to IN-1
Table 12. Analysis settings for finding nearest hospital from IN-1
Impedance Use Time Usage Time of Day Day of Week Facilities to Find
DIST Yes Start time 1700 SUN & TUE 3
FFTT Yes Start time 1700 SUN & TUE 3
TVTT Yes Start time 1700 SUN & TUE 3
Impedance Trave From U-Turns OneWay
DIST Facility to Incident Allowed Prohibited
FFTT Facility to Incident Allowed Prohibited
TVTT Facility to Incident Allowed Prohibited
Impedance Use Time Usage Time of Day Day of Week Facilities to Find
DIST Yes Start time 1700 SUN & TUE 3
FFTT Yes Start time 1700 SUN & TUE 3
TVTT Yes Start time 1700 SUN & TUE 3
Impedance Trave From U-Turns OneWay
DIST Incident to Facility Allowed Prohibited
FFTT Incident to Facility Allowed Prohibited
TVTT Incident to Facility Allowed Prohibited
54
Table 13. Results for finding nearest ground unit to IN-1
Table 14. Results for finding nearest hospital from IN-1
Figure 31. Routes from nearest ground unit to IN-1 applying DIST impedance
Cost Origin-Destination Run/Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure
DIST Clinton FD to IN-1 R1/A TU 1700 0.887 1.330 1.759 31
DIST Sunset FD to IN-1 R1/A TU 1700 1.922 2.882 3.423 31
DIST N. Davis FD West Pt to IN-1 R1/A TU 1700 2.705 5.228 7.750 31
FFTT Clinton FD to IN-1 R2/A TU 1700 0.887 1.747 2.176 32
FFTT Sunset FD to IN-1 R2/A TU 1700 1.922 4.249 4.790 32
FFTT N. Davis FD West Pt to IN-1 R2/B TU 1700 2.714 5.090 6.369 32
TVTT Clinton FD to IN-1 R3/A TU 1700 0.887 1.747 2.176 33
TVTT Sunset FD to IN-1 R3/A TU 1700 1.922 4.249 4.790 33
TVTT N. Davis FD West Pt to IN-1 R3/C TU 1700 2.715 5.692 6.885 33
Cost Origin-Destination Run/Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure
DIST IN-1 to Davis R1/A TU 1700 6.260 11.505 16.702 34
DIST IN-1 to Ogden Regional R1/A TU 1700 8.812 16.503 28.633 34
DIST IN-1 to McKay Dee R1/A TU 1700 8.932 15.583 25.452 34
FFTT IN-1 to Davis R2/B TU 1700 6.362 10.579 19.310 35
FFTT IN-1 to Ogden Regional R2/B TU 1700 8.977 16.424 27.936 35
FFTT IN-1 to McKay Dee R2/B TU 1700 8.979 18.469 24.478 35
TVTT IN-1 to Davis R3/C TU 1700 6.339 13.106 17.358 36
TVTT IN-1 to McKay Dee R3/B TU 1700 8.979 18.469 24.478 36
TVTT IN-1 to Ogden Regional R3/C TU 1700 9.150 20.199 25.955 36
55
Figure 32. Routes from nearest ground unit to IN-1 applying FFTT impedance
Figure 33. Routes from nearest ground unit to IN-1 applying TVTT impedance
56
Figure 34. Routes from IN-1 to nearest hospital applying DIST impedance
57
Figure 35. Routes from IN-1 to nearest hospital applying FFTT impedance
58
Figure 36. Routes from IN-1 to nearest hospital applying TVTT impedance
4.1.2 IN-1: Route Analysis Scenario 1
Scenario 1 (S1) is the route run and analysis from the Clinton Fire Department to
IN-1, which illustrates an ambulance on an emergency run from Clinton Fire Department
to IN-1. The analysis settings for each cost attribute used for S1 are shown in Table 15.
59
The results from S1 are divided into three sections for examination. The first section
describes the tables and figures associated with each route analysis run. The second
section explains the findings. The third section discusses the effects of the time-varying
travel time as impedance for network analysis.
Description
Six tables and four figures were created based on these runs. Tables 16 and 17
show the results of runs from Clinton FD to IN-1 applying the DIST impedance for
Sunday and Tuesday, respectively. The DIST impedance was used to optimize the
solution. The ‘DIST (mi)’ field shows the path distance expressed as the total length of
the route in miles. The ‘FFTT (min)’ field shows the accumulated free-flow travel time
value in decimal minutes. The ‘TVTT (min)’ field shows the accumulated time-varying
travel time value in decimal minutes. The values that are italicized and highlighted in red
were used for comparison purposes only and were not used to optimize the solution.
The DIST impedance route run is considered a static network analysis since the
path distance does not change through time. Therefore, Tables 16 and 17 show one
record representing all 24 time intervals. The ‘FFTT (min)’ field represents the
accumulated free-flow travel time for the route results and the ‘TVTT (min)’ field shows
the accumulated TVTT value calculated for 1700 (5:00 pm) only. Both ‘FFTT (min)’
and ‘TVTT (min)’ fields are generated without global turns delays since the global turn
restriction is not available while applying DIST as impedance.
Tables 18 and 19 show the results of runs from Clinton FD to IN-1 applying the
FFTT impedance for Sunday and Tuesday, respectively. The FFTT impedance was used
60
to optimize the solution. The FFTT impedance is considered a static network analysis
since the free-flow travel time of each road segment does not change through time.
Therefore, Tables 18 and 19 have the same value in ‘FFTT (min)’ field throughout the
run. The ‘TVTT (min)’ field shows the accumulated TVTT value calculated for the route
in each corresponding time interval.
All tables have a ‘Route’ field that represents the path created for each impedance
analysis. Though the route results (shown in the ‘Route’ field) from both DIST and
FFTT impedance runs are the same, the values in ‘FFTT (min)’ field are different when
comparing Table 16 to Table 18 and Table 17 to Table 19. The FFTT values in Tables
18 and 19 are greater than those in Tables 16 and 17. This is because global turn delays
(Section 3.3.2) were used in the FFTT impedance runs but cannot be used in the DIST
impedance runs. Start and end times are not generated when DIST is used as the
impedance but they are generated when FFTT is used as the impedance. Global turn
delays are used and reflected in the ‘FFTT (min)’ values, but they are not reflected in the
elapsed run times found in the ‘EndTime (hms)’ field. In other words, the FFTT values
will not be the same as the end times. If global turn delays were not used, these times
would be the same.
Tables 20 and 21 show the results of runs from Clinton FD to IN-1 applying the
TVTT impedance for Sunday and Tuesday, respectively. The TVTT impedance was
used to optimize the solution. Figures 37 and 38 show the travel time profiles associated
with Tables 20 and 21, respectively. They represent the TVTT when historical traffic
data is applied. Both the FFTT and TVTT values are generated with global turn delays;
therefore, they are different from those shown in Tables 16 and 17.
61
It is important to note that when TVTT is applied as impedance, the optimal route
choice (shown in the ‘Route’ field) might be varied in different time slices. Table 20
shows that there are two optimal routes choices, Route A and Route B, in the ‘Route’
field, generated by the ‘Route’ solver based on the time, day, and impedance applied.
Route A (Figure 39) is the optimal solution for Sunday from 0000 (midnight) to 1000
(10:00 am) and from 2000 (8:00 pm) to 2400 (midnight), but Route B (Figure 40) is the
optimal solution for Sunday from 1000 (10:00 am) to 2000 (8:00 pm) when TVTT is
used for impedance. The values in the ‘TVTT (min)’ field represents the accumulated
travel time of the optimal route in each time interval. The values in the ‘DIST (mi)’ and
‘FFTT (min)’ fields are adjusted corresponding to the change of route. The values in
‘DIST (mile)’ field represents the path distance in miles of the selected optimal route
(Route A or B), and the values in ‘FFTT (min)’ field represents the free-flow travel time
of the decimal minutes of the selected optimal route (Route A or B).
Table 15. Analysis settings used for S1
Table 16. Scenario 1, Sunday, Clinton FD to IN-1, DIST impedance
Table 17. Scenario 1, Tuesday, Clinton FD to IN-1, DIST impedance
Impedance Use Start Time Time of Day Day of Week Use Time Windows
DIST Yes 0000 to 2300 SUN & TUE No
FFTT Yes 0000 to 2300 SUN & TUE No
TVTT Yes 0000 to 2300 SUN & TUE No
Impedance Reorder Stops U-Turns OneWay
DIST No Allowed Prohibited
FFTT No Allowed Prohibited
TVTT No Allowed Prohibited
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A Clinton FD to IN-1 0.887 1.330 2.242
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A Clinton FD to IN-1 0.887 1.330 1.759
62
Table 18. Scenario 1, Sunday, Clinton FD to IN-1, FFTT impedance
Table 19. Scenario 1, Tuesday, Clinton FD to IN-1, FFTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Clinton FD to IN-1 0.887 1.747 1.747 0:00:00 0:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 1:00:00 1:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 2:00:00 2:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 3:00:00 3:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 4:00:00 4:01:20
A Clinton FD to IN-1 0.887 1.747 1.751 5:00:00 5:01:20
A Clinton FD to IN-1 0.887 1.747 1.759 6:00:00 6:01:20
A Clinton FD to IN-1 0.887 1.747 1.780 7:00:00 7:01:20
A Clinton FD to IN-1 0.887 1.747 1.867 8:00:00 8:01:20
A Clinton FD to IN-1 0.887 1.747 2.060 9:00:00 9:01:20
A Clinton FD to IN-1 0.887 1.747 2.317 10:00:00 10:01:20
A Clinton FD to IN-1 0.887 1.747 2.581 11:00:00 11:01:20
A Clinton FD to IN-1 0.887 1.747 2.777 12:00:00 12:01:20
A Clinton FD to IN-1 0.887 1.747 2.829 13:00:00 13:01:20
A Clinton FD to IN-1 0.887 1.747 2.820 14:00:00 14:01:20
A Clinton FD to IN-1 0.887 1.747 2.785 15:00:00 15:01:20
A Clinton FD to IN-1 0.887 1.747 2.720 16:00:00 16:01:20
A Clinton FD to IN-1 0.887 1.747 2.659 17:00:00 17:01:20
A Clinton FD to IN-1 0.887 1.747 2.523 18:00:00 18:01:20
A Clinton FD to IN-1 0.887 1.747 2.328 19:00:00 19:01:20
A Clinton FD to IN-1 0.887 1.747 2.163 20:00:00 20:01:20
A Clinton FD to IN-1 0.887 1.747 1.871 21:00:00 21:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 22:00:00 22:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 23:00:00 23:01:20
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Clinton FD to IN-1 0.887 1.747 2.036 0:00:00 0:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 1:00:00 1:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 2:00:00 2:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 3:00:00 3:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 4:00:00 4:01:20
A Clinton FD to IN-1 0.887 1.747 2.042 5:00:00 5:01:20
A Clinton FD to IN-1 0.887 1.747 2.045 6:00:00 6:01:20
A Clinton FD to IN-1 0.887 1.747 2.083 7:00:00 7:01:20
A Clinton FD to IN-1 0.887 1.747 2.157 8:00:00 8:01:20
A Clinton FD to IN-1 0.887 1.747 2.162 9:00:00 9:01:20
A Clinton FD to IN-1 0.887 1.747 2.144 10:00:00 10:01:20
A Clinton FD to IN-1 0.887 1.747 2.147 11:00:00 11:01:20
A Clinton FD to IN-1 0.887 1.747 2.142 12:00:00 12:01:20
A Clinton FD to IN-1 0.887 1.747 2.138 13:00:00 13:01:20
A Clinton FD to IN-1 0.887 1.747 2.142 14:00:00 14:01:20
A Clinton FD to IN-1 0.887 1.747 2.158 15:00:00 15:01:20
A Clinton FD to IN-1 0.887 1.747 2.169 16:00:00 16:01:20
A Clinton FD to IN-1 0.887 1.747 2.176 17:00:00 17:01:20
A Clinton FD to IN-1 0.887 1.747 2.156 18:00:00 18:01:20
A Clinton FD to IN-1 0.887 1.747 2.117 19:00:00 19:01:20
A Clinton FD to IN-1 0.887 1.747 2.088 20:00:00 20:01:20
A Clinton FD to IN-1 0.887 1.747 2.053 21:00:00 21:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 22:00:00 22:01:20
A Clinton FD to IN-1 0.887 1.747 2.036 23:00:00 23:01:20
63
Table 20. Scenario 1, Sunday, Clinton FD to IN-1, TVTT impedance
Figure 37. IN-1 Scenario 1, Sunday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Clinton FD to IN-1 0.887 1.747 1.747 0:00:00 0:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 1:00:00 1:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 2:00:00 2:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 3:00:00 3:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 4:00:00 4:01:20
A Clinton FD to IN-1 0.887 1.747 1.751 5:00:00 5:01:20
A Clinton FD to IN-1 0.887 1.747 1.759 6:00:00 6:01:21
A Clinton FD to IN-1 0.887 1.747 1.780 7:00:00 7:01:22
A Clinton FD to IN-1 0.887 1.747 1.867 8:00:00 8:01:27
A Clinton FD to IN-1 0.887 1.747 2.060 9:00:00 9:01:39
B Clinton FD to IN-1 1.136 2.304 2.352 10:00:00 10:01:45
B Clinton FD to IN-1 1.136 2.304 2.373 11:00:00 11:01:46
B Clinton FD to IN-1 1.136 2.304 2.390 12:00:00 12:01:47
B Clinton FD to IN-1 1.136 2.304 2.404 13:00:00 13:01:48
B Clinton FD to IN-1 1.136 2.304 2.417 14:00:00 14:01:49
B Clinton FD to IN-1 1.136 2.304 2.428 15:00:00 15:01:50
B Clinton FD to IN-1 1.136 2.304 2.439 16:00:00 16:01:50
B Clinton FD to IN-1 1.136 2.304 2.458 17:00:00 17:01:51
B Clinton FD to IN-1 1.136 2.304 2.460 18:00:00 18:01:52
B Clinton FD to IN-1 1.136 2.304 2.431 19:00:00 19:01:50
A Clinton FD to IN-1 0.887 1.747 2.163 20:00:00 20:01:45
A Clinton FD to IN-1 0.887 1.747 1.871 21:00:00 21:01:27
A Clinton FD to IN-1 0.887 1.747 1.747 22:00:00 22:01:20
A Clinton FD to IN-1 0.887 1.747 1.747 23:00:00 23:01:20
64
Table 21. Scenario 1, Tuesday, Clinton FD to IN-1, TVTT impedance
Figure 38. IN-1 Scenario 1, Tuesday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Clinton FD to IN-1 0.887 1.747 2.036 0:00:00 0:01:37
A Clinton FD to IN-1 0.887 1.747 2.036 1:00:00 1:01:37
A Clinton FD to IN-1 0.887 1.747 2.036 2:00:00 2:01:37
A Clinton FD to IN-1 0.887 1.747 2.036 3:00:00 3:01:37
A Clinton FD to IN-1 0.887 1.747 2.036 4:00:00 4:01:37
A Clinton FD to IN-1 0.887 1.747 2.042 5:00:00 5:01:38
A Clinton FD to IN-1 0.887 1.747 2.045 6:00:00 6:01:38
A Clinton FD to IN-1 0.887 1.747 2.083 7:00:00 7:01:40
A Clinton FD to IN-1 0.887 1.747 2.157 8:00:00 8:01:44
A Clinton FD to IN-1 0.887 1.747 2.162 9:00:00 9:01:45
A Clinton FD to IN-1 0.887 1.747 2.144 10:00:00 10:01:44
A Clinton FD to IN-1 0.887 1.747 2.147 11:00:00 11:01:44
A Clinton FD to IN-1 0.887 1.747 2.142 12:00:00 12:01:43
A Clinton FD to IN-1 0.887 1.747 2.138 13:00:00 13:01:43
A Clinton FD to IN-1 0.887 1.747 2.142 14:00:00 14:01:43
A Clinton FD to IN-1 0.887 1.747 2.158 15:00:00 15:01:45
A Clinton FD to IN-1 0.887 1.747 2.169 16:00:00 16:01:45
A Clinton FD to IN-1 0.887 1.747 2.176 17:00:00 17:01:46
A Clinton FD to IN-1 0.887 1.747 2.156 18:00:00 18:01:44
A Clinton FD to IN-1 0.887 1.747 2.117 19:00:00 19:01:42
A Clinton FD to IN-1 0.887 1.747 2.088 20:00:00 20:01:40
A Clinton FD to IN-1 0.887 1.747 2.053 21:00:00 21:01:38
A Clinton FD to IN-1 0.887 1.747 2.036 22:00:00 22:01:37
A Clinton FD to IN-1 0.887 1.747 2.036 23:00:00 23:01:37
65
Figure 39. IN-1 Scenario 1, Route A
Figure 40. IN-1 Scenario 1, Route B
66
Findings
Based on the results found in Tables 16 and 17, the total distance of Route A
(Figure 39) for Sunday and Tuesday was 0.887 miles. This value is based on the DIST
impedance and represents the shortest path from Clinton FD to IN-1 for Sunday and
Tuesday. No route changes were observed based on the use of the DIST cost attribute.
When only a distance-based cost attribute is used for impedance, the result is the shortest
path between the origin and destination.
Based on the results found in Tables 18 and 19, where FFTT was used as
impedance, the total FFTT for each run was 1.747 minutes for Sunday and Tuesday. The
total distance for each run or Route A was 0.887 miles. This is the sum of all road
segments or edges associated with the route. When FFTT is used as the impedance,
historical traffic data is not used to optimize the solution; the values in the ‘TVTT (min)’
field were just calculated for comparison. No variations in DIST, FFTT, or routes were
observed based on runs for Sunday and Tuesday. Note that the DIST values are the same
as those in Tables 16 and 17 but the FFTT values are not. The difference between 1.330
value found in Tables 16 and 17 and 1.747 value found in Tables 18 and 19 is because of
the application of global turn delays (Section 3.3.2). If global turn delays were not
applied, the FFTT values in Table 18 and 19 would be 1.330, a difference of 0.417
minutes. This is a good example why accumulated values must be compared cautiously.
When only a FFTT cost attribute is used for impedance, the result is the fastest route
between the origin and the destination. In this instance, it is also the shortest route
because the total distance is the same as those distances found in Tables 16 and 17 when
the DIST impedance is applied.
67
The impedance used to create Tables 20 and 21 was the TVTT cost attribute for
Sunday and Tuesday, respectively. TVTT is derived from historical traffic data. For
Sunday (Table 20), the TVTTs for 17 of 24 time intervals are shown to vary with time.
From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300
(11:00 pm), the travel-time values are identical (1.747 minutes). These values are exactly
the same as the free-flow travel times (shown in the ‘FFTT (min)’ field) associated with
lighter traffic patterns of late evening and early morning hours on a Sunday. TVTT
values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on
Sunday time-of-day traffic patterns. Traffic congestion is believed to be the primary
reason.
The different values in the ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 20 are
due to a route change. This change occurs between the time intervals 1000 (10:00 am)
and 1900 (7:00 pm), represented as Route B in the ‘Route’ field and highlighted in
orange. The total distance for the route associated with Route A (Figure 39) is 0.887
miles which is the same as the DIST values in Tables 16 through 19. The distance value
increased slightly (0.249 miles) to 1.136 miles due to the change from Route A to Route
B (Figure 40). It indicates that Route A has a shorter distance than Route B, but it has a
longer travel time when time-varying travel times are used for impedance. Based on
Tables 16, 18 and 20, Route A would be considered as the shortest and fastest route for
Sunday traffic patterns and the optimal route for Sunday between midnight to 10:00 am
and from 8:00 pm to midnight. Route B would be considered as the optimal route for
Sunday from 10:00 am to 8:00 pm.
68
Table 21 shows the TVTT for Tuesday; the TVTTs for 17 of 24 time intervals are
shown to vary with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and
2200 (10:00 pm) to 2300 (11:00 pm), the travel-time values are identical (2.036 minutes).
These values are close to the free-flow travel times (shown in the ‘FFTT (min)’ field)
associated with lighter traffic patterns of late evening and early morning hours on a
Tuesday. TVTT values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm)
vary, however, based on Tuesday time-of-day traffic patterns. Traffic congestion is
believed to be the primary reason.
The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 21 do not indicate a route
change. Route A shown in the ‘Route’ field is constant throughout the day. The total
distance for the path associated with Route A (Figure 39) is 0.887 miles. This distance is
the same as the DIST values in Tables 16 through 19. When Tables 19 and 21 for
Tuesday are compared, the values in the ‘DIST (mi)’, ‘FFTT (min)’ and ‘TVTT (min)’
fields are the same. Although the TVTT values in Table 21 vary with time between the
time intervals 0500 (5:00 am) and 2100 (9:00 pm), they do not change enough to generate
a new route. Based on Tables 17, 19 and 21, Route A would be considered as the
shortest, fastest and most optimal route for Tuesday traffic patterns.
Discussion
Although travel distance and travel time generated by applying TVTT impedance
sometimes increased due to traffic congestion, previous research (Alazab et al. 2011,
Chien and Kuchipudi 2003, Wu et al. 2001) has demonstrated that the travel times and
routes generated within a dynamic network are still considered as more realistic than the
69
ones in a static network environment. For instance, in Route Analysis Scenario 1 for IN-
1, Route B would be considered a more realistic optimal route than Route A during the
hours of 1000 (10:00am) and 2000 (8:00 pm) for Sunday traffic pattern.
Table 22 compares the travel times for Route A and Route B for Sunday during
the hours of 1000 (10:00am) to 2000 (8:00 pm) in order to validate the assumption that
applying TVTT will yield a more optimal routing solution when compared to DIST or
FFTT. Columns ‘A-I’, ‘A-II’, and ‘A-III’ are the values in the ‘DIST (mi)’, ‘FFTT
(min)’, and ‘TVTT (min)’ fields, respectively, from Table 18. Though the route choices
from Table 18 were based on FFTT as the impedance and generated Route A as the
fastest route, the values in the ‘TVTT (min)’ field were generated by applying TVTT as
impedance, which represents the accumulated time-varying travel time for Route A in
each time interval. The values in the ‘DIST (mi)’ field were generated by applying DIST
as impedance, which represents the total lengths of the road segments in Route A.
Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST (mi)’, ‘FFTT
(min)’, and ‘TVTT (min)’ fields, respectively, from Table 20 when TVTT was applied as
the impedance and generated Route B as the optimal route. The value in the ‘DIST (mi)’
represents the total lengths of Route B. The values in the ‘FFTT (min)’ field were
generated by applying FFTT as impedance, which represents the accumulated free-flow
travel time for Route B. Columns ‘A-IV’ and ‘B-IV’ are multipliers or free-flow factors
derived from Tables 18 and 20, respectively. These free-flow factors are ratios,
calculated by dividing TVTT by FFTT (TVTT/FFTT). The lower the value of the free-
flow factor means the travel time is closer to the free-flow travel time with less traffic
congestion.
70
Table 22. IN-1 Scenario 1, Sunday, comparison of cost impedance between
Routes A and B
Comparing the DIST and FFTT value between Routes A and B within a static
network environment, Route A is a better choice with shorter distance (Column ‘A-I’ vs.
Column ‘B-I’) and less free-flow travel time (Column ‘A-II’ vs. Column ‘B-II’). When
considering a dynamic network environment with time-varying travel time, Route B is a
more optimal choice with lower travel time (Column ‘B-III’ vs. Column ‘A-III’) for
Sunday during the hours of 1000 (10:00 am) and 2000 (8:00 pm). Two exceptions take
place at the time intervals 1000 (10:00 am) and 1900 (7:00 pm); Route A has less travel
time than Route B. However, when comparing Columns ‘A-IV’ and ‘B-IV’, Route B has
a lower free-flow factor than Route A, which means there is less traffic in Route B than
in Route A. Therefore, for the hours from 10:00 am to 11:00 am, and from 7:00 pm to
8:00 pm, Route B could be considered a better or more reliable route than Route A, but
not more optimal.
A-I A-II A-III A-IV B-I B-II B-III B-IV
From (hrs) To (hrs) DIST (mi) FFTT (min) TVTT (min)Free-flow
FactorDIST (mi) FFTT (min) TVTT (min)
Free-flow
Factor
1000 1100 0.887 1.747 2.317 1.326 1.136 2.304 2.352 1.021
1100 1200 0.887 1.747 2.581 1.478 1.136 2.304 2.373 1.030
1200 1300 0.887 1.747 2.777 1.590 1.136 2.304 2.390 1.037
1300 1400 0.887 1.747 2.829 1.619 1.136 2.304 2.404 1.043
1400 1500 0.887 1.747 2.820 1.614 1.136 2.304 2.417 1.049
1500 1600 0.887 1.747 2.785 1.594 1.136 2.304 2.428 1.054
1600 1700 0.887 1.747 2.720 1.557 1.136 2.304 2.439 1.059
1700 1800 0.887 1.747 2.659 1.552 1.136 2.304 2.458 1.067
1800 1900 0.887 1.747 2.523 1.444 1.136 2.304 2.460 1.068
1900 2000 0.887 1.747 2.328 1.333 1.136 2.304 2.431 1.055
Route A Route B
71
4.1.3 IN-1: Route Analysis Scenario 2
Scenario 2 is the route run and analysis from the IN-1 to Davis Hospital. S2
represents an ambulance on an emergency run from IN-1 to Davis Hospital. The analysis
settings window is shown in Figure 30. The analysis settings for each cost attribute used
for S2 are the same as those used for S1 and are shown in Table 15.
Description
Six tables and five figures were created based on these runs. Tables 23 and 24
show the results of runs from IN-1 to Davis Hospital applying the DIST impedance for
Sunday and Tuesday, respectively. Similar to Tables 16 and 17, the ‘TVTT (min)’ field
in these tables show the accumulated TVTT value calculated for 1700 (5:00 pm) only.
Tables 25 and 26 show the results of runs from IN-1 to Davis Hospital applying
the FFTT impedance for Sunday and Tuesday, respectively. Tables 27 and 28 show the
results of runs from IN-1 to Davis Hospital applying the TVTT impedance for Sunday
and Tuesday, respectively. Figures 41 and 42 show the travel time profiles associated
with Tables 27 and 28, respectively. They represent the TVTT when historical traffic
data is applied. Routes A (Figure 43), B (Figure 44) and C (Figure 45) represent the
routes generated by the ‘Route’ solver based on the time, day and impedance applied.
Table 23. Scenario 2, Sunday, IN-1 to Davis Hospital, DIST impedance
Table 24. Scenario 2, Tuesday, IN-1 to Davis Hospital, DIST impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A IN-1 to Davis Hospital 6.260 11.505 15.081
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A IN-1 to Davis Hospital 6.260 11.505 16.702
72
Table 25. Scenario 2, Sunday, IN-1 to Davis Hospital, FFTT impedance
Table 26. Scenario 2, Tuesday, IN-1 to Davis Hospital, FFTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-1 to Davis Hospital 6.362 10.579 10.579 0:00:00 0:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 1:00:00 1:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 2:00:00 2:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 3:00:00 3:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 4:00:00 4:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.604 5:00:00 5:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.624 6:00:00 6:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.664 7:00:00 7:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.775 8:00:00 8:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.932 9:00:00 9:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.119 10:00:00 10:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.285 11:00:00 11:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.373 12:00:00 12:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.382 13:00:00 13:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.379 14:00:00 14:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.368 15:00:00 15:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.325 16:00:00 16:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.268 17:00:00 17:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.157 18:00:00 18:08:23
B IN-1 to Davis Hospital 6.362 10.579 11.008 19:00:00 19:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.911 20:00:00 20:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.695 21:00:00 21:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 22:00:00 22:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 23:00:00 23:08:23
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-1 to Davis Hospital 6.362 10.579 12.945 0:00:00 0:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 1:00:00 1:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 2:00:00 2:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 3:00:00 3:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 4:00:00 4:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.988 5:00:00 5:08:23
B IN-1 to Davis Hospital 6.362 10.579 13.413 6:00:00 6:08:23
B IN-1 to Davis Hospital 6.362 10.579 15.342 7:00:00 7:08:23
B IN-1 to Davis Hospital 6.362 10.579 18.149 8:00:00 8:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.819 9:00:00 9:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.254 10:00:00 10:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.308 11:00:00 11:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.503 12:00:00 12:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.520 13:00:00 13:08:23
B IN-1 to Davis Hospital 6.362 10.579 17.732 14:00:00 14:08:23
B IN-1 to Davis Hospital 6.362 10.579 18.304 15:00:00 15:08:23
B IN-1 to Davis Hospital 6.362 10.579 18.856 16:00:00 16:08:23
B IN-1 to Davis Hospital 6.362 10.579 19.310 17:00:00 17:08:23
B IN-1 to Davis Hospital 6.362 10.579 18.462 18:00:00 18:08:23
B IN-1 to Davis Hospital 6.362 10.579 16.608 19:00:00 19:08:23
B IN-1 to Davis Hospital 6.362 10.579 14.935 20:00:00 20:08:23
B IN-1 to Davis Hospital 6.362 10.579 13.447 21:00:00 21:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 22:00:00 22:08:23
B IN-1 to Davis Hospital 6.362 10.579 12.945 23:00:00 23:08:23
73
Table 27. Scenario 2, Sunday, IN-1 to Davis Hospital, TVTT impedance
Figure 41. IN-1 Scenario 2, Sunday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-1 to Davis Hospital 6.362 10.579 10.579 0:00:00 0:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 1:00:00 1:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 2:00:00 2:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 3:00:00 3:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 4:00:00 4:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.604 5:00:00 5:08:24
B IN-1 to Davis Hospital 6.362 10.579 10.624 6:00:00 6:08:25
B IN-1 to Davis Hospital 6.362 10.579 10.664 7:00:00 7:08:28
B IN-1 to Davis Hospital 6.362 10.579 10.775 8:00:00 8:08:34
B IN-1 to Davis Hospital 6.362 10.579 10.932 9:00:00 9:08:44
B IN-1 to Davis Hospital 6.362 10.579 11.119 10:00:00 10:08:55
B IN-1 to Davis Hospital 6.362 10.579 11.285 11:00:00 11:09:05
B IN-1 to Davis Hospital 6.362 10.579 11.373 12:00:00 12:09:10
B IN-1 to Davis Hospital 6.362 10.579 11.382 13:00:00 13:09:11
B IN-1 to Davis Hospital 6.362 10.579 11.379 14:00:00 14:09:11
B IN-1 to Davis Hospital 6.362 10.579 11.368 15:00:00 15:09:10
B IN-1 to Davis Hospital 6.362 10.579 11.325 16:00:00 16:09:08
B IN-1 to Davis Hospital 6.362 10.579 11.268 17:00:00 17:09:04
B IN-1 to Davis Hospital 6.362 10.579 11.157 18:00:00 18:08:57
B IN-1 to Davis Hospital 6.362 10.579 11.008 19:00:00 19:08:49
B IN-1 to Davis Hospital 6.362 10.579 10.911 20:00:00 20:08:43
B IN-1 to Davis Hospital 6.362 10.579 10.695 21:00:00 21:08:30
B IN-1 to Davis Hospital 6.362 10.579 10.579 22:00:00 22:08:23
B IN-1 to Davis Hospital 6.362 10.579 10.579 23:00:00 23:08:23
74
Table 28. Scenario 2, Tuesday, IN-1 to Davis Hospital, TVTT impedance
Figure 42. IN-1 Scenario 2, Tuesday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-1 to Davis Hospital 6.362 10.579 12.945 0:00:00 0:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.945 1:00:00 1:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.945 2:00:00 2:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.945 3:00:00 3:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.945 4:00:00 4:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.988 5:00:00 5:10:47
B IN-1 to Davis Hospital 6.362 10.579 13.413 6:00:00 6:11:13
B IN-1 to Davis Hospital 6.362 10.579 15.342 7:00:00 7:13:09
C IN-1 to Davis Hospital 6.339 13.106 17.195 8:00:00 8:13:42
C IN-1 to Davis Hospital 6.339 13.106 17.185 9:00:00 9:13:41
C IN-1 to Davis Hospital 6.339 13.106 17.071 10:00:00 10:13:34
C IN-1 to Davis Hospital 6.339 13.106 17.090 11:00:00 11:13:35
C IN-1 to Davis Hospital 6.339 13.106 17.083 12:00:00 12:13:35
C IN-1 to Davis Hospital 6.339 13.106 17.069 13:00:00 13:13:34
C IN-1 to Davis Hospital 6.339 13.106 17.101 14:00:00 14:13:36
C IN-1 to Davis Hospital 6.339 13.106 17.211 15:00:00 15:13:43
C IN-1 to Davis Hospital 6.339 13.106 17.294 16:00:00 16:13:48
C IN-1 to Davis Hospital 6.339 13.106 17.358 17:00:00 17:13:51
C IN-1 to Davis Hospital 6.339 13.106 17.215 18:00:00 18:13:43
C IN-1 to Davis Hospital 6.339 13.106 16.918 19:00:00 19:13:25
B IN-1 to Davis Hospital 6.362 10.579 14.935 20:00:00 20:12:44
B IN-1 to Davis Hospital 6.362 10.579 13.447 21:00:00 21:11:15
B IN-1 to Davis Hospital 6.362 10.579 12.945 22:00:00 22:10:45
B IN-1 to Davis Hospital 6.362 10.579 12.945 23:00:00 23:10:45
75
Figure 43. IN-1 Scenario 2, Route A
Figure 44. IN-1 Scenario 2, Route B
76
Figure 45. IN-1 Scenario 2, Route C
Findings
Based on the results found in Tables 23 and 24, the total distance of Route A
(Figure 43) for Sunday and Tuesday was 6.260 miles. The value was based on the DIST
impedance and represents the shortest path from IN-1 to Davis Hospital for Sunday and
Tuesday. No route changes were observed based on the use of the DIST impedance.
Based on the results found in Tables 25 and 26, where FFTT was used as
impedance, the total FFTT for each run was 10.579 minutes for Sunday and Tuesday.
The total length for each run or Route B (Figure 44) was 6.362 miles. The difference in
length between Route A in Table 23 and Route B in Table 25 was 0.102 miles or 1.6%.
The difference in travel time between Route A and B was 0.926 minutes or 8.0%. The
use of FFTT as an impedance triggered the change from Route A in Table 23 to Route B
77
in Table 25 with relatively small differences in the path length and travel time. It was
also observed that Route B makes use of a more direct route taking advantage of
Interstate 15 (I-15) with greater speed limits when compared to Route A. No variations
in DIST or FFTT were observed based on runs for Sunday and Tuesday.
The impedance used to create Tables 27 and 28 was the TVTT cost attribute for
Sunday and Tuesday, respectively. Table 27 shows the TVTT for Sunday; the TVTTs for
17 of 24 time intervals are shown to vary with time. From the time intervals 0000
(midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time
values are identical (10.579 minutes) as free-flow travel times in the ‘FFTT (min)’ field.
These values illustrate lighter traffic patterns of late evening and early morning hours on
a Sunday. TVTT values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm)
vary based on Sunday time-of-day traffic patterns.
The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 27 do not indicate a route
change. Route B shown in the ‘Route’ field is constant throughout the day. The total
distance for Route B is 6.362 miles. This distance is the same as the DIST values in
Tables 25 and 26. When Tables 25 and 27 for Sunday are compared, the values in the
‘DIST (mi)’, ‘FFTT (min)’ and ‘TVTT (min)’ fields are the same. Although the TVTT
values in Table 27 vary with time between the time intervals 0500 (5:00 am) and 2100
(9:00 pm), they do not change enough to generate a new route. Route B, based on the
TVTT impedance for Sunday, would be considered as the optimal route. Based on
Tables 23, 25 and 27, Route A would be considered as the shortest path and Route B
would be considered as the fastest and most optimal route for Sunday traffic patterns.
78
For Tuesday (Table 28), the TTVTs for 17 of 24 time intervals are shown to vary
with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00
pm) to 2300 (11:00 pm), the travel-time values are identical (12.945 minutes). These
values are close to the free-flow travel times (shown in the ‘FFTT (min)’ field) associated
with lighter traffic patterns of late evening and early morning hours on a Tuesday. TVTT
values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on
Tuesday time-of-day traffic patterns.
The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 28 indicate a route change.
This change occurs between the time intervals 0800 (8:00 am) and 1900 (7:00 pm)
denoted by Route B and Route C (Figure 45) in the ‘Route’ field. The total distance for
the route associated with Route B is 6.362 miles. This distance is the same as the DIST
values in Tables 25 through 27. The distance value decreased slightly (-0.023 miles) to
6.339 miles due to the change from Route B to Route C. These changes are based on
increased day time traffic congestion. Based on Tables 24, 26 and 28, Route A would be
considered as the shortest path and Route B would be considered as the fastest route for
Tuesday traffic patterns. Route B would also be considered as the most optimal route
between midnight and 8:00 am and from 8:00 pm to midnight, but Route C is the most
optimal route from 8:00 am to 8:00 pm for Tuesday.
Discussion
Table 29 compares travel times for Route B and Route C for Tuesday during the
hours between 8:00 am and 8:00 pm to validate that applying TVTT will yield a more
optimal routing solution. Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST
79
(mi)’, ‘FFTT (min)’, and ‘TVTT (min)’ fields, respectively, from Table 26. Columns ‘C-
I’, ‘C-II’, and ‘C-III’ are the values in the ‘DIST (mi)’, ‘FFTT (min)’, and ‘TVTT (min)’
fields, respectively, from Table 28. Columns ‘B-IV’ and ‘C-IV’ are the free-flow factors
derived from Tables 26 and 28, respectively.
Comparing the DIST and FFTT values between Routes B and C within a static
network environment, Route B is the better solution for free-flow travel times, but Route
C has shorter travel distance. When considering a dynamic network environment with
time-varying travel time, Route C is a more optimal choice with lower travel time for
Tuesday during the hours of 0800 (8:00 am) to 2000 (8:00 pm). One exception takes
place at the time interval 1900 (7:00 pm), Route B requires less travel time than Route C.
However, when compare the Columns ‘B-IV’ and ‘C-IV’, Route C has a lower free-flow
factor than Route B, which means there is less traffic in Route C than in Route B.
Therefore, for the hours between 7:00 pm and 8:00 pm, Route C could be considered a
better or more reliable route than Route B, but not more optimal.
Table 29. IN-1 Scenario 2, Tuesday, comparison of cost impedance between
Routes B and C
BI B-II B-III B-IV C-I C-II C-III C-IV
From (hrs) To (hrs) DIST (mi) FFTT (min) TVTT (min)Free-flow
FactorDIST (mi) FFTT (min) TVTT (min)
Free-flow
Factor
0800 0900 6.362 10.579 18.149 1.716 6.339 13.106 17.195 1.312
0900 1000 6.362 10.579 17.819 1.684 6.339 13.106 17.185 1.311
1000 1100 6.362 10.579 17.254 1.631 6.339 13.106 17.071 1.303
1100 1200 6.362 10.579 17.308 1.636 6.339 13.106 17.090 1.304
1200 1300 6.362 10.579 17.503 1.655 6.339 13.106 17.083 1.303
1300 1400 6.362 10.579 17.520 1.656 6.339 13.106 17.069 1.302
1400 1500 6.362 10.579 17.732 1.676 6.339 13.106 17.101 1.305
1500 1600 6.362 10.579 18.304 1.730 6.339 13.106 17.211 1.313
1600 1700 6.362 10.579 18.856 1.782 6.339 13.106 17.294 1.320
1700 1800 6.362 10.579 19.310 1.825 6.339 13.106 17.358 1.324
1800 1900 6.362 10.579 18.426 1.745 6.339 13.106 17.215 1.314
1900 2000 6.362 10.579 16.608 1.570 6.339 13.106 16.918 1.291
Route CRoute B
80
4.1.4 IN-1: Emergency Response Routing Review
In review, four maps and one table were created showing the combined results of
Scenarios 1 and 2. For each map, the dashed red line represents the emergency response
route from Clinton FD (origin) to IN-1 (destination), and the blue dashed line represents
the emergency response route from IN-1 (origin) to Davis Hospital (destination). For
comparison purposes, each route was run at 1700 (5:00 pm) for Sunday and Tuesday.
Figure 46 shows the shortest route from Clinton FD to IN-1 (S1, Route A) and
from IN-1 to Davis Hospital (S2, Route A) when the static cost attribute DIST was
applied as impedance. The results were the same for Sunday and Tuesday. No route
change was observed between Sunday and Tuesday runs. Figure 47 illustrates the fastest
route from Clinton FD to IN-1 (S1, Route A) and from IN-1 to Davis Hospital (S2, Route
B) when the static cost attribute FFTT was applied as impedance. The results were the
same for Sunday and Tuesday. No route change was observed between Sunday and
Tuesday runs. In this instance, the fastest route from Clinton FD to IN-1 (Route A) is
also the shortest route.
The optimal routes generated by the dynamic cost attribute TVTT as impedance
are shown in Figures 48 and 49. Route changes were observed between the Sunday and
Tuesday runs due to the application of historical traffic data representing traffic
congestion. Figure 48 shows the dynamic optimal route from Clinton FD to IN-1 (S1,
Route B) and from IN-1 to Davis Hospital (S2, Route B); these paths are considered as
the most optimal routes from each origin to each destination on 5:00 pm, Sunday. In this
instance, the optimal route from IN-1 to Davis Hospital (Route B) is also the fastest
route.
81
Figure 46. IN-1, combined scenarios, Sunday and Tuesday, DIST impedance
Figure 47. IN-1, combined scenarios, Sunday and Tuesday, FFTT impedance
82
Figure 48. IN-1, combined scenarios, Sunday, TVTT impedance
Figure 49. IN-1, combined scenarios, Tuesday, TVTT impedance
83
Figure 49 shows the dynamic optimal route from Clinton FD to IN-1 (S1, Route
A) and from IN-1 to Davis Hospital (S2, Route C); these paths are considered as the most
optimal routes from each origin to each destination on 5:00 pm, Sunday. In this instance,
the optimal route from Clinton FD to IN-1 (Route A) is also the shortest route.
Table 30 shows the distances and travel times associated with each route
generated for routing example IN-1, and the routes are displayed in Figures 46 through
49. This table can be used to analyze the values associated with each route. When
observing route run results, the bolded values are based on the applied impedance that
was used to optimize the solution. The accumulated values are shown in italicized red
font and are for reference and comparison only. As previously mentioned, it is important
to note that differences in travel times can occur because of the application of global turn
delays (Sections 3.3.2 and 4.1.2 IN-1).
Table 30. IN-1, combined scenarios, comparison of emergency response routes
Cost Day StartTime (h) Scenario Route Origin-Destination Dist (mi) FFTT (min) TTVT (min) Figure
DIST SU 1700 S1 A Clinton FD to IN-1 0.887 1.330 2.242 46
DIST SU 1700 S2 A IN-1 to Davis Hospital 6.260 11.505 15.081 46
DIST TU 1700 S1 A Clinton FD to IN-1 0.887 1.330 1.759 46
DIST TU 1700 S2 A IN-1 to Davis Hospital 6.260 11.505 16.702 46
FFTT SU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.659 47
FFTT SU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 11.268 47
FFTT TU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.176 47
FFTT TU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 19.310 47
TVTT SU 1700 S1 B Clinton FD to IN-1 1.136 2.304 2.458 48
TVTT SU 1700 S2 B IN-1 to Davis Hospital 6.362 10.579 11.268 48
TVTT TU 1700 S1 A Clinton FD to IN-1 0.887 1.747 2.176 49
TVTT TU 1700 S2 C IN-1 to Davis Hospital 6.339 13.106 17.358 49
84
4.2 Route Example for IN-2
4.2.1 IN-2: Closest Facility Analysis
Incident 2 (IN-2) is located in Kaysville at the intersection of Boynton and
Fairfield Roads (Table 10). The same methodology and analysis settings used in 4.1.1
IN-1: Closest Facility Analysis were applied to this routing example. As a result of these
runs and applying DIST, FFTT and TVTT as impedances, it was determined the closest
ground unit to IN-2 is Kaysville Fire Department, and the closest hospital from IN-2 is
Davis Hospital. Tables 31 and 32 indicate runs 1A, 2A and 3A are the shortest, fastest
and optimal routes, respectively. Figures 50 through 55 show the routes associated with
the cost attribute used.
Table 31. Results for finding nearest ground unit to IN-2
Table 32. Results for finding nearest hospital from IN-2
Run Cost Origin-Destination Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure
1A DIST Kaysville FD to IN-2 A TU 1700 1.038 1.900 3.277 50
1B DIST Layton FD No. 53 to IN-2 A TU 1700 1.888 3.338 6.480 50
1C DIST Layton FD No. 52 to IN-2 A TU 1700 3.985 5.978 8.639 50
2A FFTT Kaysville FD to IN-2 A TU 1700 1.038 2.517 3.894 51
2B FFTT Layton FD No. 53 to IN-2 B TU 1700 1.953 4.013 4.839 51
2C FFTT Layton FD No. 52 to IN-2 A TU 1700 3.985 8.011 10.673 51
2A TVTT Kaysville FD to IN-2 B TU 1700 1.277 2.879 3.431 52
2B TVTT Layton FD No. 53 to IN-2 B TU 1700 1.953 4.013 4.839 52
2C TVTT Layton FD No. 52 to IN-2 A TU 1700 3.985 8.011 10.673 52
Run Cost Origin-Destination Route Day Time DIST (mi) FFTT (min) TVTT (min) Figure
1A DIST IN-2 to Davis Hospital A TU 1700 5.055 7.620 15.095 53
1B DIST IN-2 to McKay Dee A TU 1700 11.464 15.855 31.475 53
1C DIST IN-2 to Ogden Regional A TU 1700 11.551 15.509 30.022 53
2A FFTT IN-2 to Davis Hospital B TU 1700 5.895 7.696 18.733 54
2B FFTT IN-2 to Ogden Regional B TU 1700 12.130 17.774 28.431 54
2C FFTT IN-2 to McKay Dee B TU 1700 11.987 17.936 29.039 54
2A TVTT IN-2 to Davis Hospital C TU 1700 5.338 10.538 13.485 55
2B TVTT IN-2 to Ogden Regional C TU 1700 12.659 20.547 29.147 55
2C TVTT IN-2 to McKay Dee C TU 1700 12.515 20.709 29.755 55
85
Figure 50. Routes from nearest ground unit to IN-2 applying DIST impedance
86
Figure 51. Routes from nearest ground unit to IN-2 applying FFTT impedance
87
Figure 52. Routes from nearest ground unit to IN-2 applying TVTT impedance
88
Figure 53. Routes from IN-2 to nearest hospital applying DIST impedance
89
Figure 54. Routes from IN-2 to nearest hospital applying FFTT impedance
90
Figure 55. Routes from IN-2 to nearest hospital applying TVTT impedance
91
4.2.2 IN-2: Route Analysis Scenario 1
Scenario 1 is the route run and analysis from Kaysville Fire Department to IN-2.
S1 represents an ambulance on an emergency run from Kaysville Fire Department to IN-
2. The same methodology and analysis settings used in 4.1.2 IN-1: Route Analysis
Scenario 1 were applied to this route analysis.
Description
Six tables and four figures were created based on these runs. Tables 33 and 34
show the results of runs from Kaysville FD to IN-2 applying the DIST impedance for
Sunday and Tuesday, respectively. Similar to Tables 23 and 24, the ‘TVTT (min)’ field
in these tables shows the accumulated TVTT value calculated for 1700 (5:00 pm) only.
Tables 35 and 36 show the results of runs from Kaysville FD to IN-2 applying the
FFTT impedance for Sunday and Tuesday, respectively. Tables 37 and 38 show the
results of runs from Kaysville FD to IN-2 applying the TVTT impedance for Sunday and
Tuesday, respectively. Figures 56 and 57 show the travel time profiles associated with
Tables 37 and 38, respectively. Routes A and B are displayed in Figures 58 and 59,
respectively, and represent the routes generated by the ‘Route’ solver based on the time,
day and impedance applied.
Table 33. Scenario 1, Sunday, Kaysville FD to IN-2, DIST impedance
Table 34. Scenario 1, Tuesday, Kaysville FD to IN-2, DIST impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A Kaysville FD to IN-2 1.038 1.900 3.145
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A Kaysville FD to IN-2 1.038 1.900 3.277
92
Table 35. Scenario 1, Sunday, Kaysville FD to IN-2, FFTT impedance
Table 36. Scenario 1, Tuesday, Kaysville FD to IN-2, FFTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Kaysville FD to IN-2 1.038 2.517 2.517 0:00:00 0:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 1:00:00 1:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 2:00:00 2:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 3:00:00 3:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 4:00:00 4:01:54
A Kaysville FD to IN-2 1.038 2.517 2.525 5:00:00 5:01:54
A Kaysville FD to IN-2 1.038 2.517 2.539 6:00:00 6:01:54
A Kaysville FD to IN-2 1.038 2.517 2.571 7:00:00 7:01:54
A Kaysville FD to IN-2 1.038 2.517 2.698 8:00:00 8:01:54
A Kaysville FD to IN-2 1.038 2.517 2.963 9:00:00 9:01:54
A Kaysville FD to IN-2 1.038 2.517 3.312 10:00:00 10:01:54
A Kaysville FD to IN-2 1.038 2.517 3.668 11:00:00 11:01:54
A Kaysville FD to IN-2 1.038 2.517 3.926 12:00:00 12:01:54
A Kaysville FD to IN-2 1.038 2.517 3.992 13:00:00 13:01:54
A Kaysville FD to IN-2 1.038 2.517 3.981 14:00:00 14:01:54
A Kaysville FD to IN-2 1.038 2.517 3.936 15:00:00 15:01:54
A Kaysville FD to IN-2 1.038 2.517 3.847 16:00:00 16:01:54
A Kaysville FD to IN-2 1.038 2.517 3.762 17:00:00 17:01:54
A Kaysville FD to IN-2 1.038 2.517 3.576 18:00:00 18:01:54
A Kaysville FD to IN-2 1.038 2.517 3.309 19:00:00 19:01:54
A Kaysville FD to IN-2 1.038 2.517 3.087 20:00:00 20:01:54
A Kaysville FD to IN-2 1.038 2.517 2.690 21:00:00 21:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 22:00:00 22:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 23:00:00 23:01:54
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Kaysville FD to IN-2 1.038 2.517 2.709 0:00:00 0:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 1:00:00 1:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 2:00:00 2:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 3:00:00 3:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 4:00:00 4:01:54
A Kaysville FD to IN-2 1.038 2.517 2.723 5:00:00 5:01:54
A Kaysville FD to IN-2 1.038 2.517 2.794 6:00:00 6:01:54
A Kaysville FD to IN-2 1.038 2.517 3.148 7:00:00 7:01:54
A Kaysville FD to IN-2 1.038 2.517 3.686 8:00:00 8:01:54
A Kaysville FD to IN-2 1.038 2.517 3.639 9:00:00 9:01:54
A Kaysville FD to IN-2 1.038 2.517 3.527 10:00:00 10:01:54
A Kaysville FD to IN-2 1.038 2.517 3.540 11:00:00 11:01:54
A Kaysville FD to IN-2 1.038 2.517 3.564 12:00:00 12:01:54
A Kaysville FD to IN-2 1.038 2.517 3.562 13:00:00 13:01:54
A Kaysville FD to IN-2 1.038 2.517 3.600 14:00:00 14:01:54
A Kaysville FD to IN-2 1.038 2.517 3.712 15:00:00 15:01:54
A Kaysville FD to IN-2 1.038 2.517 3.813 16:00:00 16:01:54
A Kaysville FD to IN-2 1.038 2.517 3.894 17:00:00 17:01:54
A Kaysville FD to IN-2 1.038 2.517 3.735 18:00:00 18:01:54
A Kaysville FD to IN-2 1.038 2.517 3.391 19:00:00 19:01:54
A Kaysville FD to IN-2 1.038 2.517 3.090 20:00:00 20:01:54
A Kaysville FD to IN-2 1.038 2.517 2.809 21:00:00 21:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 22:00:00 22:01:54
A Kaysville FD to IN-2 1.038 2.517 2.709 23:00:00 23:01:54
93
Table 37. Scenario 1, Sunday, Kaysville FD to IN-2, TVTT impedance
Figure 56. IN-2 Scenario 1, Sunday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Kaysville FD to IN-2 1.038 2.517 2.517 0:00:00 0:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 1:00:00 1:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 2:00:00 2:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 3:00:00 3:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 4:00:00 4:01:54
A Kaysville FD to IN-2 1.038 2.517 2.525 5:00:00 5:01:55
A Kaysville FD to IN-2 1.038 2.517 2.539 6:00:00 6:01:55
A Kaysville FD to IN-2 1.038 2.517 2.571 7:00:00 7:01:57
A Kaysville FD to IN-2 1.038 2.517 2.698 8:00:00 8:02:05
A Kaysville FD to IN-2 1.038 2.517 2.963 9:00:00 9:02:21
A Kaysville FD to IN-2 1.038 2.517 3.312 10:00:00 10:02:42
A Kaysville FD to IN-2 1.038 2.517 3.668 11:00:00 11:03:03
A Kaysville FD to IN-2 1.038 2.517 3.926 12:00:00 12:03:19
A Kaysville FD to IN-2 1.038 2.517 3.992 13:00:00 13:03:23
A Kaysville FD to IN-2 1.038 2.517 3.981 14:00:00 14:03:22
A Kaysville FD to IN-2 1.038 2.517 3.936 15:00:00 15:03:19
A Kaysville FD to IN-2 1.038 2.517 3.847 16:00:00 16:03:14
A Kaysville FD to IN-2 1.038 2.517 3.762 17:00:00 17:03:09
A Kaysville FD to IN-2 1.038 2.517 3.576 18:00:00 18:02:58
A Kaysville FD to IN-2 1.038 2.517 3.309 19:00:00 19:02:42
A Kaysville FD to IN-2 1.038 2.517 3.087 20:00:00 20:02:28
A Kaysville FD to IN-2 1.038 2.517 2.690 21:00:00 21:02:04
A Kaysville FD to IN-2 1.038 2.517 2.517 22:00:00 22:01:54
A Kaysville FD to IN-2 1.038 2.517 2.517 23:00:00 23:01:54
94
Table 38. Scenario 1, Tuesday, Kaysville FD to IN-2, TVTT impedance
Figure 57. IN-2 Scenario 1, Tuesday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
A Kaysville FD to IN-2 1.038 2.517 2.709 0:00:00 0:02:06
A Kaysville FD to IN-2 1.038 2.517 2.709 1:00:00 1:02:06
A Kaysville FD to IN-2 1.038 2.517 2.709 2:00:00 2:02:06
A Kaysville FD to IN-2 1.038 2.517 2.709 3:00:00 3:02:06
A Kaysville FD to IN-2 1.038 2.517 2.709 4:00:00 4:02:06
A Kaysville FD to IN-2 1.038 2.517 2.723 5:00:00 5:02:06
A Kaysville FD to IN-2 1.038 2.517 2.794 6:00:00 6:02:11
B Kaysville FD to IN-2 1.277 2.879 3.246 7:00:00 7:02:25
B Kaysville FD to IN-2 1.277 2.879 3.394 8:00:00 8:02:34
B Kaysville FD to IN-2 1.277 2.879 3.402 9:00:00 9:02:34
B Kaysville FD to IN-2 1.277 2.879 3.366 10:00:00 10:02:32
B Kaysville FD to IN-2 1.277 2.879 3.374 11:00:00 11:02:32
B Kaysville FD to IN-2 1.277 2.879 3.362 12:00:00 12:02:32
B Kaysville FD to IN-2 1.277 2.879 3.354 13:00:00 13:02:31
B Kaysville FD to IN-2 1.277 2.879 3.362 14:00:00 14:02:32
B Kaysville FD to IN-2 1.277 2.879 3.396 15:00:00 15:02:34
B Kaysville FD to IN-2 1.277 2.879 3.416 16:00:00 16:02:35
B Kaysville FD to IN-2 1.277 2.879 3.431 17:00:00 17:02:36
B Kaysville FD to IN-2 1.277 2.879 3.392 18:00:00 18:02:34
B Kaysville FD to IN-2 1.277 2.879 3.313 19:00:00 19:02:29
B Kaysville FD to IN-2 1.277 2.879 3.256 20:00:00 20:02:25
A Kaysville FD to IN-2 1.038 2.517 2.809 21:00:00 21:02:12
A Kaysville FD to IN-2 1.038 2.517 2.709 22:00:00 22:02:06
A Kaysville FD to IN-2 1.038 2.517 2.709 23:00:00 23:02:06
95
Figure 58. IN-2 Scenario 1, Route A
Figure 59. IN-2 Scenario 1, Route B
96
Findings
Based on the results found in Tables 33 and 34, the total distance of Route A
(Figure 58) for Sunday and Tuesday was 1.038 miles. Based on the results found in
Tables 35 and 36, where FFTT was used as impedance, the total FFTT for each run was
the same at 2.517 minutes for Sunday and Tuesday. The total length for each run or
Route A (Figure 58) was 1.038 miles. No variations in DIST, FFTT, or routes were
observed based on runs for Sunday and Tuesday. In this instance, the fastest route is also
the shortest route from Kaysville FD to IN-2 (S1, Route A).
The impedance used to create Tables 37 and 38 was the TVTT cost attribute for
Sunday and Tuesday, respectively. For Sunday (Table 37), the TVTTs for 17 of 24 time
intervals are shown to vary with time. From the time intervals 0000 (midnight) to 0400
(4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time values are identical
(2.517 minutes). The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 37 do not indicate a
route change. Based on Tables 33, 35 and 37, Route A would be considered the shortest,
fastest, and most optimal route for Sunday traffic patterns.
For Tuesday (Table 38), the TVTTs for 17 of 24 time intervals are shown to vary
with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00
pm) to 2300 (11:00 pm), the travel-time values are identical (2.709 minutes). The ‘DIST
(mi)’ and ‘FFTT (min)’ fields in Table 38 indicate a route change. This change occurs
between the time intervals 0700 (7:00 am) and 2000 (8:00 pm), represented as Route B
(Figure 59) in the ‘Route’ field and highlighted in orange. The distance value increased
slightly (0.239 miles) to 1.277 miles due to the change from Route A to Route B. Based
on Tables 34, 36 and 38, Route A would be considered the shortest and fastest route for
97
Tuesday traffic patterns. Route A would also be considered as the most optimal route
from midnight to 7:00 am and from 9:00 pm to midnight, but Route B is the most optimal
route from 7:00 am to 9:00 pm for Tuesday.
Discussion
Table 39 compares travel times for Route A and Route B for Tuesday during the
hours between 7:00 am and 9:00 pm. Route A is a better choice with shorter distance and
less free-flow travel time when comparing the static DIST and FFTT values. Route B is
a more optimal choice with lower travel time for Tuesday during the hours of 7000 (7:00
am) and 2100 (9:00 pm). Two exceptions take place at the time intervals 0700 (7:00 am)
and 2000 (8:00 pm), when Route A has less travel time than Route B, but Route B has a
lower free-flow factor with less traffic than Route A. Therefore, for the hours from 7:00
am to 8:00 am, and from 8:00 pm to 9:00 pm, Route B could be considered a better or
more reliable route than Route A, but not more optimal.
Table 39. IN-2 Scenario 1, Tuesday, comparison of cost impedance between
Routes A and B
A-I A-II A-III A-IV B-I B-II B-III B-IV
From (hrs) To (hrs) DIST (mi) FFTT (min) TVTT (min)Free-flow
FactorDIST (mi) FFTT (min) TVTT (min)
Free-flow
Factor
0700 0800 1.038 2.517 3.148 1.251 1.277 2.879 3.246 1.127
0800 0900 1.038 2.517 3.686 1.464 1.277 2.879 3.394 1.179
0900 1000 1.038 2.517 3.639 1.446 1.277 2.879 3.402 1.182
1000 1100 1.038 2.517 3.527 1.401 1.277 2.879 3.366 1.169
1100 1200 1.038 2.517 3.540 1.406 1.277 2.879 3.374 1.172
1200 1300 1.038 2.517 3.564 1.416 1.277 2.879 3.362 1.168
1300 1400 1.038 2.517 3.562 1.415 1.277 2.879 3.354 1.165
1400 1500 1.038 2.517 3.600 1.430 1.277 2.879 3.362 1.168
1500 1600 1.038 2.517 3.712 1.475 1.277 2.879 3.396 1.180
1600 1700 1.038 2.517 3.813 1.515 1.277 2.879 3.416 1.187
1700 1800 1.038 2.517 3.894 1.547 1.277 2.879 3.431 1.192
1800 1900 1.038 2.517 3.735 1.484 1.277 2.879 3.392 1.178
1900 2000 1.038 2.517 3.391 1.347 1.277 2.879 3.313 1.151
2000 2100 1.038 2.517 3.090 1.228 1.277 2.879 3.256 1.131
Route A Route B
98
4.2.3 IN-2: Route Analysis Scenario 2
Scenario 2 is the route run and analysis from IN-2 to Davis Hospital. S2
represents an ambulance on an emergency run from IN-2 to Davis Hospital. The same
methodology and analysis settings used in 4.1.3 IN-1: Route Analysis Scenario 2 were
applied to this route analysis.
Description
Six tables and seven figures were created based on these runs. Tables 40 and 41
show the results of runs from IN-2 to Davis Hospital applying the DIST impedance for
Sunday and Tuesday, respectively. Similar to Tables 33 and 34, the ‘TVTT (min)’ field
in these tables show the accumulated TVTT value calculated for 1700 (5:00 pm) only.
Tables 42 and 43 show the results of runs from IN-2 to Davis Hospital applying
the FFTT impedance for Sunday and Tuesday, respectively. Tables 44 and 45 show the
results of runs from IN-2 to Davis Hospital applying the TVTT impedance for Sunday
and Tuesday, respectively. Figures 60 and 61 show the travel time profiles associated
with Tables 44 and 45, respectively. Routes A (Figures 62), B (Figures 63), C (Figures
64), D (Figures 65), and E (Figures 66) represent the routes generated by the ‘Route’
solver based on the time, day and impedance applied.
Table 40. Scenario 2, Sunday, IN-2 to Davis Hospital, DIST impedance
Table 41. Scenario 2, Tuesday, IN-2 to Davis Hospital, DIST impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A IN-2 to Davis Hospital 5.055 7.620 11.938
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min)
A IN-2 to Davis Hospital 5.055 7.620 15.095
99
Table 42. Scenario 2, Sunday, IN-2 to Davis Hospital, FFTT impedance
Table 43. Scenario 2, Tuesday, IN-2 to Davis Hospital, FFTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-2 to Davis Hospital 5.895 7.696 8.288 0:00:00 0:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 1:00:00 1:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 2:00:00 2:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 3:00:00 3:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 4:00:00 4:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.330 5:00:00 5:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.368 6:00:00 6:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.445 7:00:00 7:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.685 8:00:00 8:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.081 9:00:00 9:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.574 10:00:00 10:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.040 11:00:00 11:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.333 12:00:00 12:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.389 13:00:00 13:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.376 14:00:00 14:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.332 15:00:00 15:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.213 16:00:00 16:06:46
B IN-2 to Davis Hospital 5.895 7.696 10.076 17:00:00 17:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.798 18:00:00 18:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.413 19:00:00 19:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.131 20:00:00 20:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.566 21:00:00 21:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 22:00:00 22:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.288 23:00:00 23:06:46
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-2 to Davis Hospital 5.895 7.696 8.871 0:00:00 0:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 1:00:00 1:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 2:00:00 2:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 3:00:00 3:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 4:00:00 4:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.942 5:00:00 5:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.594 6:00:00 6:06:46
B IN-2 to Davis Hospital 5.895 7.696 12.580 7:00:00 7:06:46
B IN-2 to Davis Hospital 5.895 7.696 16.940 8:00:00 8:06:46
B IN-2 to Davis Hospital 5.895 7.696 16.437 9:00:00 9:06:46
B IN-2 to Davis Hospital 5.895 7.696 15.557 10:00:00 10:06:46
B IN-2 to Davis Hospital 5.895 7.696 15.643 11:00:00 11:06:46
B IN-2 to Davis Hospital 5.895 7.696 15.938 12:00:00 12:06:46
B IN-2 to Davis Hospital 5.895 7.696 15.960 13:00:00 13:06:46
B IN-2 to Davis Hospital 5.895 7.696 16.287 14:00:00 14:06:46
B IN-2 to Davis Hospital 5.895 7.696 17.177 15:00:00 15:06:46
B IN-2 to Davis Hospital 5.895 7.696 18.031 16:00:00 16:06:46
B IN-2 to Davis Hospital 5.895 7.696 18.733 17:00:00 17:06:46
B IN-2 to Davis Hospital 5.895 7.696 17.419 18:00:00 18:06:46
B IN-2 to Davis Hospital 5.895 7.696 14.546 19:00:00 19:06:46
B IN-2 to Davis Hospital 5.895 7.696 11.961 20:00:00 20:06:46
B IN-2 to Davis Hospital 5.895 7.696 9.652 21:00:00 21:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 22:00:00 22:06:46
B IN-2 to Davis Hospital 5.895 7.696 8.871 23:00:00 23:06:46
100
Table 44. Scenario 2, Sunday, IN-2 to Davis Hospital, TVTT impedance
Figure 60. IN-2 Scenario 2, Sunday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
B IN-2 to Davis Hospital 5.895 7.696 8.288 0:00:00 0:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.288 1:00:00 1:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.288 2:00:00 2:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.288 3:00:00 3:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.288 4:00:00 4:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.330 5:00:00 5:07:24
B IN-2 to Davis Hospital 5.895 7.696 8.368 6:00:00 6:07:26
B IN-2 to Davis Hospital 5.895 7.696 8.445 7:00:00 7:07:31
B IN-2 to Davis Hospital 5.895 7.696 8.685 8:00:00 8:07:45
C IN-2 to Davis Hospital 5.686 7.412 8.672 9:00:00 9:08:04
C IN-2 to Davis Hospital 5.686 7.412 9.038 10:00:00 10:08:26
C IN-2 to Davis Hospital 5.686 7.412 9.366 11:00:00 11:08:46
C IN-2 to Davis Hospital 5.686 7.412 9.548 12:00:00 12:08:57
C IN-2 to Davis Hospital 5.686 7.412 9.571 13:00:00 13:08:58
C IN-2 to Davis Hospital 5.686 7.412 9.563 14:00:00 14:08:58
C IN-2 to Davis Hospital 5.686 7.412 9.541 15:00:00 15:08:56
C IN-2 to Davis Hospital 5.686 7.412 9.455 16:00:00 16:08:51
C IN-2 to Davis Hospital 5.686 7.412 9.346 17:00:00 17:08:45
C IN-2 to Davis Hospital 5.686 7.412 9.132 18:00:00 18:08:32
C IN-2 to Davis Hospital 5.686 7.412 8.842 19:00:00 19:08:15
C IN-2 to Davis Hospital 5.686 7.412 8.647 20:00:00 20:08:03
C IN-2 to Davis Hospital 5.686 7.412 8.226 21:00:00 21:07:38
B IN-2 to Davis Hospital 5.895 7.696 8.288 22:00:00 22:07:21
B IN-2 to Davis Hospital 5.895 7.696 8.288 23:00:00 23:07:21
101
Table 45. Scenario 2, Tuesday, IN-2 to Davis Hospital, TVTT impedance
Figure 61. IN-2 Scenario 2, Tuesday travel time profile, TVTT impedance
Route Origin-Destination DIST (mi) FFTT (min) TVTT (min) StartTime (hms) EndTime (hms)
C IN-2 to Davis Hospital 5.686 7.412 8.417 0:00:00 0:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.417 1:00:00 1:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.417 2:00:00 2:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.417 3:00:00 3:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.417 4:00:00 4:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.494 5:00:00 5:07:54
C IN-2 to Davis Hospital 5.686 7.412 9.249 6:00:00 6:08:39
D IN-2 to Davis Hospital 5.524 10.734 12.429 7:00:00 7:10:05
E IN-2 to Davis Hospital 5.338 10.538 13.279 8:00:00 8:10:51
E IN-2 to Davis Hospital 5.338 10.538 13.278 9:00:00 9:10:51
E IN-2 to Davis Hospital 5.338 10.538 13.122 10:00:00 10:10:41
E IN-2 to Davis Hospital 5.338 10.538 13.151 11:00:00 11:10:43
E IN-2 to Davis Hospital 5.338 10.538 13.130 12:00:00 12:10:42
D IN-2 to Davis Hospital 5.524 10.734 13.021 13:00:00 13:10:40
E IN-2 to Davis Hospital 5.338 10.538 13.148 14:00:00 14:10:43
E IN-2 to Davis Hospital 5.338 10.538 13.297 15:00:00 15:10:52
E IN-2 to Davis Hospital 5.338 10.538 13.404 16:00:00 16:10:58
E IN-2 to Davis Hospital 5.338 10.538 13.485 17:00:00 17:11:03
E IN-2 to Davis Hospital 5.338 10.538 13.297 18:00:00 18:10:52
D IN-2 to Davis Hospital 5.524 10.734 12.788 19:00:00 19:10:26
D IN-2 to Davis Hospital 5.524 10.734 12.437 20:00:00 20:10:05
C IN-2 to Davis Hospital 5.686 7.412 9.310 21:00:00 21:08:43
C IN-2 to Davis Hospital 5.686 7.412 8.417 22:00:00 22:07:49
C IN-2 to Davis Hospital 5.686 7.412 8.417 23:00:00 23:07:49
102
Figure 62. IN-2 Scenario 2, Route A
Figure 63. IN-2 Scenario 2, Route B
103
Figure 64. IN-2 Scenario 2, Route C
Figure 65. IN-2 Scenario 2, Route D
104
Figure 66. IN-2 Scenario 2, Route E
Findings
Based on the results found in Tables 40 and 41, the total distance of Route A
(Figure 62) for Sunday and Tuesday was 5.055 miles. No route changes were observed
based on the use of the DIST impedance. Based on the results found in Tables 42 and 43,
where FFTT was used as impedance, the total FFTT for each run was 7.696 minutes for
Sunday and Tuesday. The total length for each run or Route B (Figure 63) was 5.895
miles. No variations in DIST, FFTT, or routes were observed based on runs for Sunday
and Tuesday. The use of FFTT as an impedance triggered the change from Route A in
Tables 40 and 41 to Route B in Table 42 and 43. It was also observed that Route B takes
more advantage of Interstate 15 (I-15) when compared to Route A.
105
The impedance used to create Tables 44 and 45 was the TVTT cost attribute for
Sunday and Tuesday, respectively. Table 44 shows the TVTT for Sunday; the TVTTs for
17 of 24 time intervals are shown to vary with time. From the time intervals 0000
(midnight) to 0400 (4:00 am) and 2200 (10:00 pm) to 2300 (11:00 pm), the travel-time
values are identical (8.288 minutes) and close to the corresponding FFTT values. TVTT
values between the time intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on
Sunday time-of-day traffic patterns. The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table
44 indicate a route change. This change occurs between the time interval 0900 (9:00 am)
and 2100 (9:00 pm) denoted by Route C (Figure 64) in the ‘Route’ field. The distance
value decreased slightly (-0.209 miles) to 5.686 miles due to the change from Route B to
Route C.
For Tuesday (Table 45), the TTVTs for 17 of 24 time intervals are shown to vary
with time. From the time intervals 0000 (midnight) to 0400 (4:00 am) and 2200 (10:00
pm) to 2300 (11:00 pm), the travel-time values are identical (8.417 minutes). TVTT
values between the intervals 0500 (5:00 am) and 2100 (9:00 pm) vary based on Tuesday
time-of-day traffic patterns. The ‘DIST (mi)’ and ‘FFTT (min)’ fields in Table 45
indicate multiple route changes. Several changes occur between the time intervals 0700
(7:00 am) and 2000 (8:00 pm) denoted by Route D (Figures 65) and Route E (Figures
66) in the ‘Route’ field. The total distance for the route associated with Route C is 5.686
miles. The distance value decreased slightly (-0.162 miles) to 5.524 miles due to the
change from Route C to Route D. The distance value decreased even more (-0.348
miles) to 5.338 miles due to the change from Route C to Route E. The difference in
distance between Route D and Route E is 0.186 miles.
106
Discussion
Table 46 compares travel times for Routes A, B, and C for Sunday during the
hours between 9:00 am and 10:00 pm to validate that applying TVTT will yield a more
optimal routing solution. Column ‘A-I’ is the value in the ‘DIST (mi)’ field from Table
40. Columns ‘B-I’, ‘B-II’, and ‘B-III’ are the values in the ‘DIST (mi)’, ‘FFTT (min)’,
and ‘TVTT (min)’ fields, respectively, from Table 42. Columns ‘C-I’, ‘C-II’, and ‘C-III’
are the values in the ‘DIST (mi)’, ‘FFTT (min)’, and ‘TVTT (min)’ fields, respectively,
from Table 44. Columns ‘B-IV’ and ‘C-IV’ are the free-flow factors derived from Tables
40 and 42, respectively.
Comparing the DIST and FFTT values between Routes A, B, and C within a static
network environment, Route A (Figure 62) is the best solution for the shortest distance,
and Route C (Figure 64) seems to be the best solution for free-flow travel times (7.412
minutes). However, from the route analysis applying FFTT as impedance (Table 42),
ArcGIS Network Analyst Route Solver generated Route B (Figure 63) as the fastest route
based on static free-flow travel time (7.696 minutes). According to Esri (2013g), “the
best route can be defined as the route that has the lowest impedance, where the
impedance is chosen by the users.” Therefore, Route B should be the fastest route based
on the static free-flow time (FFTT impedance) from IN-2 to Davis Hospital. There
should not be any other route with less free-flow time. Table 40 shows Route A with less
free-flow travel time (7.620 minutes) due to DIST impedance route analysis, which does
not consider the global turn restriction. Route C was generated by applying TVTT as
impedance as the optimal route during the hours between 9:00 am and 10:00 pm within a
dynamic network environment, but its FFTT value (7.412 minutes) is way less than
107
Routes A and B, which raises the question of which route is actually the fastest route.
This inconsistent route analysis was re-run several times through different versions of
ArcGIS Network Analyst (9.3, 10, and 10.) to ensure no human error in parameter inputs,
but all re-runs produced the exact same results. Route C based on TVTT impedance has
less free-flow travel time than Route B generated through FFTT impedance. According
to Esri (2013f), the users “can accumulate any number of impedance attributes in a route
analysis, but accumulated attributes don’t play a role in computing the path along the
network.” The FFTT values in Table 44 is just accumulated free-flow times attributes, so
it might not be a reliable result for the fastest route. Without further investigation on
ArcGIS Network Analyst shortest path algorithm, the fastest route can’t be determined
for IN-2 Scenario 2 for Sunday traffic pattern.
Comparing TVTT and Free-flow Factor between Routes B and C with a dynamic
network environment with time-varying travel time, Route C is the optimal route during
the hours between 9:00 am and 10:00 pm. Route C requires less travel time than Route B
(TVTT values) and has lower free-flow factors in each time interval shown in Table 46.
Table 46. IN-2 Scenario 2, Sunday, comparison of cost impedance between
Routes A, B, and C
Route A
A-I B-I B-II B-III B-IV C-I C-II C-III C-IV
From
(hrs)
To
(hrs)DIST (mi) DIST (mi) FFTT (min) TVTT (min)
Free-flow
FactorDIST (mi) FFTT (min) TVTT (min)
Free-flow
Factor
0900 1000 5.055 5.895 7.696 9.081 1.180 5.686 7.412 8.672 1.170
1000 1100 5.055 5.895 7.696 9.574 1.244 5.686 7.412 9.038 1.219
1100 1200 5.055 5.895 7.696 10.040 1.305 5.686 7.412 9.366 1.264
1200 1300 5.055 5.895 7.696 10.333 1.343 5.686 7.412 9.548 1.288
1300 1400 5.055 5.895 7.696 10.389 1.350 5.686 7.412 9.571 1.291
1400 1500 5.055 5.895 7.696 10.376 1.348 5.686 7.412 9.563 1.290
1500 1600 5.055 5.895 7.696 10.332 1.343 5.686 7.412 9.541 1.287
1600 1700 5.055 5.895 7.696 10.213 1.327 5.686 7.412 9.455 1.276
1700 1800 5.055 5.895 7.696 10.076 1.309 5.686 7.412 9.346 1.261
1800 1900 5.055 5.895 7.696 9.798 1.273 5.686 7.412 9.132 1.232
1900 2000 5.055 5.895 7.696 9.413 1.223 5.686 7.412 8.842 1.193
2000 2100 5.055 5.895 7.696 9.131 1.186 5.686 7.412 8.647 1.167
2100 2200 5.055 5.895 7.696 8.566 1.113 5.686 7.412 8.226 1.110
Route B Route C
108
Table 47 is the summary report for the shortest, fastest, and optimal route from
IN-2 to Davis Hospital for Tuesday traffic pattern. Route A (Figure 62) is the shortest
route (Table 41) with the travel distance as 5.055 miles. Route B (Figure 63) can be
considered as the fastest route (7.696 minutes) while applying FFTT as impedance (Table
43). However, Route C (Figure 64) based on TVTT as impedance (Table 45) has less
free-flow travel time (7.412 minutes) than Route B. Without further investigation, the
fastest route can’t be determined for IN-2 Scenario 2 for Tuesday traffic pattern. Route C
(Figure 64) is the optimal route during the hours from midnight to 7:00 am, and from
9:00 pm to midnight (Table 45). Route D (Figure 65) is the optimal route during the
hours from 7:00 am to 8:00 am, from 1:00 pm to 2:00 pm, and from 7:00 pm to 9:00 pm
(Table 45). Route E (Figure 66) is the optimal route during the hours from 8:00 am to
1:00 pm, and from 2:00 pm to 7:00 pm (Table 45).
Table 47. IN-2 Scenario 2, Tuesday, summary of cost impedance between Routes A, B, C,
D, and E
Routes DIST (mi) FFTT (min) TVTT (min) Remarks
Route A 5.055 7.620 Shortest route
Route B 5.895 7.696 8.871-18.733Fastest route based on FFTT
impedance
Route C 5.686 7.412 8.417-9.310Optimal route between time intervals
0000-0600 and 2100-2300
Route D 5.524 10.734 12.429-12.788Optimal route in time intervals 0700,
1300, 1900, and 2000
Route E 5.338 10.538 13.122-13.485Optimal route between time intervals
0800-1200 and 1400-1800
109
4.2.4 IN-2: Emergency Response Routing Review
In review, four maps and one table were created showing the combined results of
Scenarios 1 and 2. For each map, the dashed red line represents the emergency response
route from Kaysville FD (origin) to IN-2 (destination) and the blue dashed line represents
the emergency response route from IN-2 (origin) to Davis Hospital (destination). For
comparison purposes, each route was run at 1700 (5:00 pm) for Sunday and Tuesday.
Figure 67 shows the shortest route from Kaysville FD to IN-2 (S1, Route A) and
from IN-2 to Davis Hospital (S2, Route A) when the static cost attribute DIST was
applied as impedance. The results were the same for Sunday and Tuesday. No route
change was observed between Sunday and Tuesday runs. Figure 68 illustrates the fastest
route from Kaysville FD to IN-2 (S1, Route A) and from IN-2 to Davis Hospital (S2,
Route B) when the static cost attribute FFTT was applied as impedance. In this instance,
the fastest route from Kaysville FD to IN-2 (Route A) is also the shortest route.
However, the fastest route from IN-2 to Davis Hospital (Route B) might not be a reliable
result as discussed in 4.2.3.
The optimal routes generated by the dynamic cost attribute TVTT as impedance
are shown in Figures 69 and 70. Route changes were observed between and during the
Sunday and Tuesday runs due to the application of historical traffic data representing
traffic congestion. Figure 69 shows the dynamic optimal path from Kaysville FD to IN-2
(S1, Route A) and from IN-2 to Davis Hospital (S2, Route C). These paths are
considered the most optimal routes from each origin to each destination on 5:00 pm,
Sunday. In this instance, the optimal route from Kaysville FD to IN-2 (Route A) is also
the shortest and fastest route.
110
Figure 67. IN-2, combined scenarios, Sunday and Tuesday, DIST impedance
Figure 68. IN-2, combined scenarios, Sunday and Tuesday, FFTT impedance
111
Figure 69. IN-2, combined scenarios, Sunday, TVTT impedance
Figure 70. IN-2, combined scenarios, Tuesday, TVTT impedance
112
Figure 70 shows the dynamic optimal route from the path from Kaysville FD to
IN-2 (S1, Route B) and from IN-2 to Davis Hospital (S2, Route E) on 5:00 pm, Tuesday.
S2, Route D is an additional route change that represents the optimal route on 7:00 pm,
Tuesday from IN-2 to Davis Hospital. These paths are considered the most optimal
routes from each origin to each destination on Tuesday for their specified time intervals.
Table 48 shows the distances and travel times associated with each route
generated for routing example IN-2 and are displayed in Figures 67 through 70. This
table can be used to analyze the values associated with each route. When observing route
run results, the bolded values are based on the applied impedance that was used to
optimize the solution. The accumulated values are shown in italicized red font and are
for reference and comparison only.
Table 48. IN-2, combined scenarios, comparison of emergency response routes
Cost Day StartTime (h) Scenario Route Origin-Destination Dist (mi) FFTT (min) TTVT (min) Figure
DIST SU 1700 S1 A Kaysville FD to IN-2 1.038 1.900 3.145 67
DIST SU 1700 S2 A IN-2 to Davis Hospital 5.055 7.620 11.938 67
DIST TU 1700 S1 A Kaysville FD to IN-2 1.038 1.900 3.277 67
DIST TU 1700 S2 A IN-2 to Davis Hospital 5.055 7.620 15.095 67
FFTT SU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.762 68
FFTT SU 1700 S2 B IN-2 to Davis Hospital 5.895 7.696 10.076 68
FFTT TU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.894 68
FFTT TU 1700 S2 B IN-2 to Davis Hospital 5.895 7.696 18.733 68
TVTT SU 1700 S1 A Kaysville FD to IN-2 1.038 2.517 3.762 69
TVTT SU 1700 S2 C IN-2 to Davis Hospital 5.686 7.412 9.346 69
TVTT TU 1700 S1 B Kaysville FD to IN-2 1.277 2.879 3.431 70
TVTT TU 1300 S2 D IN-2 to Davis Hospital 5.524 10.734 13.021 70
TVTT TU 1700 S2 E IN-2 to Davis Hospital 5.338 10.538 13.485 70
113
4.3 Discussion of Results
On the whole, the results seemed to agree with the expectations and meet the
objective of the study. The DIST impedance generated the shortest path with no regard
to travel time. The FFTT impedance generated the quickest or fastest path, and the
TVTT generated the best or most optimal path by applying historical traffic data.
There are three apparent inconsistent outcomes in the analysis results. First is the
travel time calculation while employing the ‘Start Time’ option in the analysis setting for
ArcGIS Network Analyst ‘Route’ solver (Figure 30). Theoretically, the results of ‘End
Time’ should be the sum of ‘Start Time’ and the travel time in the specified time interval,
but the results from this study showed different outcomes. See Table 18 (FFTT
impedance) as an example. In the time interval from 0200 (2:00 am) to 0300 (3:00 am),
the travel time is 1.747 decimal minutes or 00:01:45 (hms), therefore, the ‘End Time’
should be 2:01:45 (hms) instead of 2:01:20 (hms). This inconsistency can be observed
throughout the entire study. With further investigation, it was discovered that the ‘End
Time’ was calculated by the travel time (for both FFTT and TVTT) without global turn
delays. The ‘FFTT (min)’ field in Table 16 represents the accumulated free-flow travel
time for the same route shown in Table 18 without global turn delays. The travel time is
1.330 decimal minutes or 00:01:20 (hms), which is exactly the same elapsed time from
‘StartTime (hms)’ to ‘EndTime (hms)’ shown in Table 18.
The second inconsistent outcome is the determination of the best route while
applying TVTT as impedance. According to Esri (2013g), the best route is the result
with the lowest impedance. See Table 22 as an example. In the time intervals 1000
(10:00 am) to 1100 (11:00 am) and 1900 (7:00 pm) to 2000 (8:00 pm), Route B is the
114
best route generated from TVTT impedance (Table 20), but Route A, generated from
FFTT impedance (Table 18), has a lower TVTT value than Route B. The explanation
can be made that Route B has a lower free-flow factor than Route A, but based on Esri’s
(2013g) document, the best route should be determined by the user’s specified
impedance, which is TVTT, not the free-flow factor.
The third inconsistent outcome is the accumulated impedance values generated
when a particular impedance was not used to optimize the route analysis. See Table 42
as an example. The fastest route from IN-2 to Davis Hospital, while applying FFTT
impedance, is Route B with a free-flow travel time of 7.696 minutes. However, applying
the TVTT impedance generated an optimal route, Route C, for the time intervals between
0900 (9:00 am) and 2100 (9:00 pm), (Table 44). The accumulated FFTT value for Route
C is less than Route B’s free-flow travel time. If the calculations of other accumulated
impedances through TVTT route analysis (such as DIST and FFTT from Table 44) are
correct, then Route C should be the best route results from FFTT route analysis not Route
B.
Even with these three inconsistent outcomes, this project still demonstrates that
the routes and response times for emergency response vehicles could change due to
variations in traffic flow related to the day (e.g., weekday or weekend) and the time of
day (traffic congestion). The shortest route might not be the most efficient path for
emergency vehicles. Although emergency vehicle routing can at times exceed the normal
speed limit, FFTT impedance route analysis can also serve as the surrogate of road class
(generally roads with multiple lanes have higher speed limits, which makes it easier for
emergency vehicles to pass other vehicles), which is a factor when considering traffic
115
conditions and the necessity of passing other vehicles. Traffic conditions are not static;
they are dependent on the time and day. TVTT impedance route analysis could provide a
more realistic simulation than DIST and FFTT impedance route analysis. The optimal
route from IN-2 to Davis Hospital (4.2.3) changed based on the time of the day (Table
45). A decrease in travel time by a few minutes might not be significant for normal
traffic, but when considering emergency vehicle routing, it can be a matter of life and
death.
Although a fundamental aim of this study was to illustrate how a dynamic
network is preferred over a static network when applied to emergency response routing,
this research was nevertheless theoretic in nature. Regardless of how accurate the
network data is, or how many variables, restrictions, and impedances were applied to
generate the most realistic and best path, decisions made by an experienced emergency
response vehicle driver in real time under real traffic scenarios will always outweigh a
computer generated routing model. However, dynamic emergency response routing as
shown in this research can be valuable for generating preliminary routes from an origin to
a destination then modified by an experienced emergency response vehicle driver as the
situation demands.
116
Chapter 5: Conclusion and Future Improvements
5.1 Conclusion
The objective of this research was to observe, record, and analyze changes to routes
and travel/response-times of emergency vehicles due to variations in traffic flow related to
traffic congestion on certain days of the week and times of day. It was believed that
dynamic routing based on cost attributes derived from historical travel-time data and
applied to network edges could help response vehicles avoid congested areas and improve
travel times (Kok et al. 2012, Panahi and Delavar 2009). As mentioned in the literature
review, because travel congestion affects the travel time of emergency vehicles and
increases response times, time-dependent variables derived from traffic count data could
realistically represent peak-hour traffic congestion and help emergency vehicles avoid
these congested areas and improve travel time (Kok et al. 2012, Panahi and Delavar
2009).
The results of this analysis indicate that when the DIST impedance was used by the
‘Route’ solver, it generated the shortest path between the origin and the destination in both
scenarios. When the FFTT impedance was applied, it generated the quickest or fastest
route. When the TVTT impedance was used, it generated the best or most optimal path
under realistic traffic conditions.
The project was overall a success and the research objectives were met. This
project was able to utilize the shortest path algorithms in Esri’s Network Analyst to
calculate the shortest, fastest, and the most optimal routes by applying various cost
attributes or impedances to practical vehicle emergency response scenarios. Differences in
117
route directions, travel times, and distances were observed and analyzed based on these
impedances and the findings were discussed in detail explaining the results.
5.2 Limitations
Six noticeable limitations associated with this research are discussed in this
section. The lack of experience in the creation and application of traffic profiles was one
limitation. Scarcity of literature about the origin of and how free-flow multipliers are
generated and incorporated into a spatio-temporal database and the actual implementation
of traffic profiles was another limitation. The main source of information on the creation
and use of traffic profiles was from Esri. Other literature did not detail the making of
traffic profiles. Several inquiries to private corporations and government organizations
for clarification were not very successful. Answers to questions that would be helpful
include: What is the origin and background of historical traffic profiles? What
methodology is used to create the free-flow factors or multipliers? Is there a scientific
approach for relating traffic volume profiles created from ATR site data to free-flow
traffic profiles stored in the ‘DailyProfiles_Time_60min’ table?
Another issue that limited the study was the coarseness or resolution of the
historical traffic data. UDOT traffic volume data was only available in 60 minute time
intervals. The original Esri free-flow traffic profile (‘DailyProfiles’) table was available
in 5 minute time slices. Modifications had to be made to accommodate UDOT traffic
volume data and generate the ‘DailyProfiles_Time_60min’ table used in this research. A
loss in granularity resulted from this modification. It is believed that the precision and
correctness of travel times and routes would be improved and better represent traffic
118
conditions using smaller time intervals, however, there would be a downside. For
research purposes it would increase the number of runs per 24 hour period from 24 to
288. This would impact the size and configuration of the tables and increase the work
load associated with executing the runs and the route analysis.
Road segment classification was another limitation and concern. The
methodology used to select and match ATR sites to the Urban Area Functional
Classification system was based on limited information and guidance. It is unclear if the
methodology used in this research was the most suitable approach. Questions that
surfaced were: Is one classification system preferred over another when creating a
transportation network? Is there a better or perhaps a more systematic approach to the
classification of road segments? Is there a better process to match ATR sites to a
classification system?
The study was also limited in the sense that certain dynamic variables that would
have improved the network and routing scenarios were not used due to time, availability
and the complexity of implementation. Examples include seasonal weather conditions,
road conditions, number of lanes, slope, etc.
The final limitation was the lack of transparency in Esri’s shortest path algorithm.
Esri (2013g) maintains the best route is determined based on the lowest impedance.
While applying TVTT as impedance in this study, there were several exceptions where
the new route’s TVTT was higher than the route based on FFTT, although the free-flow
factor values were lower. These results are inconsistent with Esri’s (2013g) statement.
Examples can be found in Table 22, time intervals 1000 (10:00 am) to 1100 (11:00 am)
and 1900 (7:00 pm) to 2000 (8:00 pm); Table 29, time interval 1900 (7:00 pm) to 2000
119
(8:00 pm); and Table 39, time intervals 0700 (7:00 am) to 0800 (8:00 am) and 2000 (8:00
pm) to 2100 (9:00 pm). Another inconsistent result is the fastest path from IN-2 to Davis
Hospital. The TVTT route analysis generated a route (Route C, Figure 64) with a lower
free-flow travel time (Tables 44 and 45) than the solution (Route B, Figure 63) produced
from FFTT route analysis (Tables 42 and 43). These inconsistences cannot be explained
without further investigation of Esri’s shortest path algorithm. However, there is
insufficient documentation from Esri to describe how Dijkstra’s algorithm was
implemented in ArcGIS Network Analyst.
5.3 Challenges and Solutions
One challenge both in time and complexity was the preparation and maintenance
of the road network dataset. As explained in Section 3.2, additional work was needed to
prepare the road network for analysis. Preparation included directionality, connectivity
and adding one way restrictions to limit travel on one way roads and avoid routing
irregularities. Routes overshooting an expected ramp, going the wrong way on a
freeway, entering or exiting the wrong way on a ramp or overshooting an entrance into
the hospital because of junction and road segment errors were a few challenges that
needed to be addressed.
The solutions to these challenges required hours of editing road edges, junctions,
and associated attribute fields for the network to function properly. More experience
might have made this process easier and less time consuming. Identifying an error or
irregularity, repairing it through digitization or re-attribution, rebuilding the network
dataset and testing was the general pattern. For instance, after a road segment was added,
120
deleted or edited in some manner such as merging or splitting segments, certain fields
had to be recalculated. Some field attributes also had to be copied to the
‘Project_Profiles’ join table so the historical traffic data would function correctly. If a
speed limit was changed in the ‘ProjectArea’ feature class, the same change had to be
made in the ‘Project_Profiles’ join table. Travel times also had to be re-calculated. After
these changes, the network dataset had to be rebuilt. To aid in the process, a relationship
class was created between the ‘ProjectArea’ feature class and the ‘Project_Profiles’ join
table and proved very useful. The relationship class is mentioned in Section 3.3 and one
way restrictions are explained in Section 3.3.1.
5.4 Future Improvements
This research has shown how a GIS was used to solve a shortest path problem
with respect to emergency vehicle response routing. Certain network attributes and
attribute values were omitted or not used to their fullest potential for this research. It was
not practical nor was this research meant to cover all aspects of network analysis.
Several future improvements could make the road network and subsequent analysis more
functional and realistic. In actuality, improvements to a road network and shortest path
are boundless. A continuation of this research might include the following
improvements:
1. Explore the feasibility of incorporating average annual daily traffic (AADT),
vehicle miles traveled (VMT), peak hourly volume (PHV), or other measures
of traffic capacity as alternatives ways to model traffic congestion.
2. Apply elevations or Z values to highway and other overpasses.
3. Incorporate slope values especially on the mountain front benches.
121
4. Improve road classifications and incorporate road hierarchy.
5. Incorporate traffic lanes.
6. Incorporate more specified ‘restricted turns’ modeled from a turn feature class
versus the generalized use of global turn delays.
7. Incorporate barriers and other restrictions to resemble areas of road construction,
traffic calming measures, weather conditions, etc.
8. Fine tune the use of one way restrictions.
9. Explore and compare other route solvers available in Esri Network Analyst.
10. Compare results to real world emergency response call data.
One additional future improvement might be to expand this study and develop an
efficient low-cost web-based emergency response routing system that can incorporate
real-time or live traffic data based on using GPS technology. This system could be used
by local EMS dispatch agencies to improve response times for not only lower level
medical priority dispatches but for higher level emergency situations or disasters that can
affect large areas and cause significantly more casualties.
122
References
Abkowitz, M., Walsh, S., Hauser, E., and Minor, L., 1990. Adaptation of geographic
information systems to highway management. Journal of Transportation
Engineering, 116 (3), 310-327.
Alazab, A., Venkatraman, S., Abawajy, J., and Alazab, M., 2011. An optimal
transportation routing approach using GIS-based dynamic traffic flows. 3rd
International Conference on Information and Financial Engineering IPEDR 12
(2011). IACSIT Press, Singapore, 172-178.
Alivand, M., Alesheikh, A., and Malek, M., 2008. New method for finding optimal path
in dynamic networks. World Applied Sciences Journal, 3 (1), 25-33.
Chien, S., and Kuchipudi, C., 2003. Dynamic travel time prediction with real-time and
historic data. Journal of Transportation Engineering, 129 (6) 608-616.
Cormen, T., Leiserson, C., Rivest, R., and Stein, C., 2001. Single-source shortest paths.
In: Introduction to algorithms. 2nd ed. Cambridge, MA: MIT Press, 581-635.
Cova, T., 1999. GIS in emergency management. In: P.A. Longley, M.F. Goodchild, D.J.
Maguire, D.W. Rhind, eds. Geographical Information Systems: Principles,
Techniques, Applications, and Management. New York, NY: John Wiley & Sons,
845-858.
Curtin, K., 2007. Network analysis in geographic information science: review,
assessment, and projections. Cartography and Geographic Information Science,
34 (2), 103-111.
Davis County Emergency Management Services, 2009. Davis County Emergency
Operations Plan [online]. Available from:
http://www.co.davis.ut.us/sheriff/divisions/emergency_services/emergency_mana
gement/documents/Emergency%20Operations%20Plan/Basic%20Plan.pdf
[Accessed 16 March 2012].
Demiryurek, U., Pan, B., Banaei-Kashani, F., and Shahabi, C., 2009. Towards modeling
the traffic data on road networks. In: Proceedings of the Second International
Workshop on Computational Transportation Science. November 3, 2009, Seattle,
WA, 13-18.
Demiryurek, U., Banaei-Kashani, F., and Shahabi, C., 2010. A case for time-dependent
shortest path computation in spatial networks. 18th SIGSPATIAL International
Conference on Advances in Geographic Information Systems, 2-5 November 2010
San Jose, CA. New York: ACM, 474-477.
123
Esri, 2012. Historical traffic data [online]. ArcGIS 10.0 Help. Available from:
http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//0047000000s0000
000 [Accessed 12 July 2012].
Esri, 2013a. Origins of modeling historical traffic data [online]. Forum: Network
Analyst. Available from: http://forums.arcgis.com/threads/85338-Origins-of-
modeling-historical-traffic-data [Accessed 24 May 2013].
Esri, 2013b. What is a network dataset? [online]. ArcGIS 10.1 Help. Available from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000007000000
[Accessed 3 August 2013].
Esri, 2013c. Building a network dataset in ArcCatalog [online]. ArcGIS 10.1 Help.
Available from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000010000000
[Accessed 3 August 2013].
Esri, 2013d. Understanding network attributes [online]. ArcGIS 10.1 Help. Available
from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//00470000000m00000
0#GUID-4BAE3856-0B23-4D4B-937F-7C2B01FEB426 [Accessed 12 June
2013].
Esri, 2013e. About global turns [online]. ArcGIS 10.1 Help. Available from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000030000000
[Accessed 12 June 2013].
Esri, 2013f. Route analysis [online]. ArcGIS 10.1 Help. Available from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000045000000
[Accessed 12 June 2013].
Esri, 2013g. Types of network analysis layers [online]. ArcGIS 10.1 Help. Available
from:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000032000000
[Accessed 23 October 2013]
Federal Highway Administration (FHWA), 1989. Functional Classification Guidelines:
Concepts, Criteria, and Procedures [Online]. Available from:
http://www.fhwa.dot.gov/planning/processes/statewide/related/functional_classifi
cation/fc02.cfm (Accessed 22 February 2013).
Federal Highway Administration (FHWA), 2013. Planning for Operations [online].
Available from: http://ops.fhwa.dot.gov/publications/fhwahop10003/cs2.htm
[Accessed 27 January 2013].
124
George, B., Sangho, K., and Shekhar, S., 2007. Spatio-temporal network databases and
routing algorithms: a summary of results. In: D. Papadias, D. Zhang, and G.
Kollios, eds. 10th International Conference on Advances in Spatial and Temporal
Databases, 16-18 July 2007 Boston, MA. Berlin-Heidelberg: Springer-Verlag,
460-477.
Goodchild, M., 2000. GIS and transportation: status and challenges. Geoinformatica, 4
(2), 127-139.
Granberg, B., 2011. Draft: Utah Road Network Solutions Dataset [online]. Available
from: http://old.gis.utah.gov [Accessed 4 January 2012].
Haghani, A., Hu, H., and Tian, Q., 2003. An optimization model for real-time emergency
vehicle dispatching and routing. In: Proceedings of the 82nd Annual Meeting of
the Transportation Research Board (CD-ROM), Washington, DC: National
Research Council.
Huang, B. and Pan, X., 2007. GIS coupled with traffic simulation and optimization for
incident response. Computers, Environment and Urban Systems, 31 (2), 116-132.
International Association of Fire Chiefs (IAFC), 2013. Guide to IAFC model policies and
procedures for emergency vehicle safety [online]. Available from:
http://www.iafc.org/files/downloads/VEHICLE_SAFETY/VehclSafety_IAFCpol
AndProceds.pdf [Accessed 14 August 2013].
Jones, S., 2013. UDOT Traffic and Safety. GRAMMA Request for 2010 Crash Statistics
[email]. (Personal Communication, 11 January 2012).
Kamga, C., Mouskos, K., and Paaswell, R., 2011. A methodology to estimate travel time
using dynamic traffic assignment (DTA) under incident conditions.
Transportation Research: Part C, 19 (6), 1215-1224.
Karadimas, N., Kolokathi, M., Defteraiou, G., and Loumos, V., 2007. Municipal waste
collection of large items optimized with ArcGIS Network Analyst. In:
Proceedings 21st European Conference on Modelling and Simulation, 4-6 June
2007 Prague, Czech Republic, Curran Associates, Inc., 80-85.
Kim, D., Park, D., Rho, J., Baek, S., and Namkoong, S., 2007. A study on the
construction of past travel time pattern for freeway travel time forecasting-
focused on loop detectors. International Journal of Urban Sciences, 11 (1), 14-29.
Kim, S., Lewis, M., and White, C., 2005. Optimal vehicle routing with real-time traffic
information. IEEE Intelligent Transportation Systems, 6 (2), 178-188.
Kok, A., Hans, E., and Schutten, J., 2012. Vehicle routing under time-dependent travel
times: the impact of congestion avoidance. Computers & Operations Research, 39
(5), 910-918.
125
Li, X., and Lin, H., 2003. A data model for moving object in dynamic road network. In:
Proceedings of the 3rd
International Symposium on Digital Earth, September 2003
Brno, Czech Republic, 459-471. Available from
http://umdrive.memphis.edu/xli1/www/index_files/15.pdf [Accessed 2 August
2013].
Lim, Y. and Kim, H., 2005. A shortest path algorithm for real road network based on path
overlap. Journal of the Eastern Asia Society for Transportation Studies, 6 (1),
1426-1438.
McDonald, P., 2013. Layton City Fire Department. Discussion on ambulance routing.
[Conversation] (Personal communication, 30 June 2013)
Nadi, S. and Delavar, M., 2003. Spatio-temporal modeling of dynamic phenomena in
GIS. ScanGIS 2003 Proceeding, 215-225.
Nannicini, G., 2009. Point to point shortest paths on dynamic time-dependent road
networks. Thesis (PhD). Ecole Polytechnique, Palaiseau, France.
Naqi, A., Akhter, N., and Ali, N., 2010. Developing components of web GIS for shortest
path analysis “Find Shortest Route”: A geographical explanation for SSGC,
Pakistan. Sindh University Research Journal, 42 (1), 23-30.
National Highway Traffic Safety Administration Emergency Medical Services (NHTSA
EMS), 2013. What Is EMS? [online]. Available from:
http://www.ems.gov/whatisEMS.htm [Accessed 8 May 2013].
Nichol, K., 2010. Highway functional classification, the what, why and how [online].
Utah Department of Transportation. Available from:
http://dixiempo.files.wordpress.com/2010/09/functional_classification.pdf
[Accessed 8 January 2013).
Niemeier, D., Utts, J., and Fay, L., 2002. Cluster analysis for optimal sampling of traffic
count data: air quality example. Journal of Transportation Engineering, 128(1),
97-102.
Panahi, S. and Delavar, M., 2008. A GIS-based dynamic shortest path determination in
emergency vehicles. World Applied Sciences Journal, 3 (1), 88-94.
Panahi, S. and Delavar, M., 2009. Dynamic shortest path in ambulance routing based on
GIS. International Journal of Geoinformatics, 5 (1), 13-19.
Park, D., Shin, H., Hong, S., and Jung, C., 2005. The use of historical data for travel time
forecasting in the advanced traveler information system. Journal of the Eastern
Asia Society for Transportation Studies, 6 (1), 2473-2486.
126
Puthuparampil, M., 2007. Report Dijkstra's Algorithm [online]. Unpublished
Presentation, Computer Science Department, New York University. Available
from: http://www.cs.nyu.edu/courses/summer07/G22.2340-
001/Presentations/Puthuparampil.pdf [Accessed 11 May 2013].
Riad, A., El-Mikkawy, M., and Shabana, B., 2012. Real time route for dynamic road
congestions. International Journal of Computer Science Issues, 9 (3), 423-428.
Sadeghi-Niaraki, A., Varshosaz, M., Kim, K., and Jung, J., 2011. Real world
representation of a road network for route planning in GIS. Expert Systems with
Applications, 38 (10), 11999-12008.
Shaw, S-L., 2000. Moving toward spatiotemporal GIS for transportation applications. In:
Proceedings of the 20th ESRI User Conference. Available from
http://proceedings.esri.com/library/userconf/proc00/professional/papers/PAP205/
p205.htm [Accessed 16 May 2012].
Shaw, S-L., 2010. Geographic information systems for transportation: from a static past
to a dynamic future. Annals of GIS, 16 (3), 129-140.
Tele Atlas, 2009. Speed profiles: Intelligent data for optimal routing [online]. Available
from: http://www.tele-mart.com/documents/SpeedProfilesInfoSheettm.pdf
[Accessed 27 May 2013].
Thirumalaivasan, D., and Guruswamy, V., 1997. Optimal route analysis using GIS.
Available from:
http://www.gisdevelopment.net/application/Utility/transport/utilitytr0004pf.htm
[Accessed 23 May 2012].
TomTom, 2012. Speed profiles [online]. Available from:
https://www.tomtom.com/en_gb/licensing/products/traffic/historical-traffic/speed-
profiles/ [Accessed 27 May 2013).
United States Census Bureau, 2012. State and County QuikFacts [online]. Available
from: http://quickfacts.census.gov [Accessed 14 July 2012].
Utah Automated Geographic Reference Center (Utah AGRC), 2012. Utah State
Geographic Information Database [online]. Available from: http://gis.utah.gov/
[Accessed 9 July 2012].
Utah Bureau of Emergency Medical Services (Utah BEMS), 2012a. Designated
Emergency Medical Dispatch Agencies [online]. Available from:
http://health.utah.gov/ems/providers/dispatchlist.php [Accessed 12 June 2012].
Utah Bureau of Emergency Medical Services (Utah BEMS), 2012b. Licensed and
Designated EMS Agencies [online]. Available from:
http://health.utah.gov/ems/providers/providerlist.php [Accessed 12 June 2012].
127
Utah Bureau of Emergency Medical Services (Utah BEMS), 2012c. Trauma Centers
[online]. Available from: http://health.utah.gov/ems/trauma/trauma_centers.html
[Accessed 12 June 2012].
Utah Department of Transportation (UDOT), 2001. Revisions to the Federal-Aid-Eligible
Highway System UDOT 07-25 [online]. Available from:
http://www.udot.utah.gov/main/uconowner.gf?n=10481900321937357 [Accessed
2 January 2013].
Utah Department of Transportation (UDOT), 2008. Davis Weber East-West
Transportation Study Legislative Report. Project Number 070188. Prepared by
InterPlan Co. Midvale, Utah. Available from:
http://www.udot.utah.gov/main/uconowner.gf?n=2370414092093187 [Accessed
10 July 2012].
Utah Department of Transportation (UDOT), 2010. Hourly Traffic Volume Report for
April 2010 [online]. Available from:
http://www.udot.utah.gov/main/uconowner.gf?n=14314902222840185 [Accessed
20 March 2013].
Utah Department of Transportation (UDOT), 2012. Ogden - Layton Urbanized
Functional Class System Map [online]. Available from:
http://www.udot.utah.gov/main/uconowner.gf?n=135846111387468374
[Accessed 2 January 2013].
Wilde, E., 2009. Do emergency medical system response times matter for health
outcomes? Health Economics, 21 (8), 1-86.
Wu, Y., Miller, H., and Hung, M., 2001. A GIS-based decision support system for
analysis of route choice in congested urban road networks. Journal of
Geographical Systems, 3 (1), 3-24.