Comparative Analysis of the Multi-Modal Transportation Environment in the Northgate and Capitol Hill Urban Centers
Submitted by:
David Perlmutter Daniel Rowe
December 8, 2009
URBDP 422: Geospatial Analysis
Professor Marina Alberti, Matt Marsik
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Table of Contents
Introduction and Project Summary ................................................................................................. 3 Project Questions ............................................................................................................................ 4 Methodology ................................................................................................................................... 4
Bicycle Metrics ........................................................................................................................... 5 Bike Lane Miles per Road Mile .............................................................................................. 5 Average ADT (Average Daily Traffic) per Bike Lane Mile .................................................. 6 Average Vehicle Speed Limit per Bike Lane Mile ................................................................. 7
Pedestrian Metrics ....................................................................................................................... 7 Diversity of Land Uses in the Pedestrian Environment .......................................................... 7 Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per Acre ......................................................................................................................................... 9 Average Vehicle Speed per Sidewalk Mile .......................................................................... 10
Transit Metrics .......................................................................................................................... 10 Number of Living Units within ¼ mile of a Transit Stop per Square Mile .......................... 10 Average Service Frequency per Route ................................................................................. 11 Average Service Span per Route .......................................................................................... 11
Table 1: Summary of Metrics .................................................................................................... 12 Analysis and Interpretation of Results .......................................................................................... 12
Analysis..................................................................................................................................... 12 Bike ....................................................................................................................................... 13 Walk ...................................................................................................................................... 13 Transit ................................................................................................................................... 15
Limitations ................................................................................................................................ 15 Implications............................................................................................................................... 16
Appendix A: Project Maps............................................................................................................ 18 Appendix B: Data Dictionary ....................................................................................................... 34 Works Cited .................................................................................................................................. 36
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Introduction and Project Summary The Puget Sound is experiencing rapid growth in population and employment, especially in its
urban centers, which have been identified by the Puget Sound Regional Council (PSRC) as areas
to focus this growth. As these areas grow and become denser, it will be critical to maintain high
levels of mobility to ensure the efficient movement of people and goods. It is anticipated that
roadways alone will not be able to meet this additional demand. To create a healthy and
prosperous region, the PSRC urban centers will need to invest in a multi-modal transportation
network, including transit, bike and walk facilities and services. As our centers begin to develop
this network, it will be important to benchmark and measure the success of each investment.
Multi-modal level of service (LOS) is an emerging concept aimed at developing metrics to
measure such investments. Multi-modal LOS metrics are used to evaluate various transportation
modes and impacts. LOS, or quality of service, refers to the speed, convenience, comfort and
security of transportation facilities and services as experienced by users. Employing LOS
measurements will be a valuable exercise for urban centers to track their progress in creating
multi-modal transportation networks to meet the needs of the growing population.
Our research uses GIS analysis to explore different metrics that can be applied to multi-modal
LOS measurements. Our research is not intended to calculate an LOS score or to make definitive
statements about different alternative transportation environments, like some recent studies have
attempted, but it aims to identify and calculate different metrics for alternative modes of
transportation and evaluate the effectiveness of each metric in measuring LOS. This research will
measure qualities and levels of multi-modal transportation service in two different Urban Centers
in Seattle, WA. We used three indicators to measure each alternative mode of transportation in
the Northgate and Capitol Hill Urban Centers. In total, our research has explored nine indicators:
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three for transit, three for bike, and three for pedestrian travel. The indicators and methodology
are described below in the Methodology section. The resulting analysis of the metrics enables a
more in-depth comparative analysis of the two urban centers by comparing the multi-modal
environment, as opposed to measuring each mode separately. Our research utilizes metrics
identified in peer-reviewed literature from the transportation planning field. This project aims to
identify the principal differences in the alternative transportation environments of the Capitol
Hill and Northgate Urban Centers, as well as evaluating the effectiveness of the nine metrics we
have employed in our analysis of the bike, pedestrian, and transit infrastructure within these
centers.
Project Questions Our project questions are the following:
• What is the difference in alternative transportation environments in the Northgate and
Capitol Hill Urban Centers?
• How effective are the nine metrics in determining the alternative transportation
environment?
Methodology For the purposes of clarity in our methodology, the importance of separating our methodology
into bike, pedestrian, and transit segments was clear from the beginning of this project. Although
some of the literature provided examples of the methods to produce a function that would
synthesize the indicators of all three modes of transportation to create a single figure representing
multi-modal LOS, our research was limited by available data and time and focused on an
exploration of different metrics and their effectiveness in measuring each mode. As a result of
our limited time to complete this research, our analysis focused on three metrics per mode of
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transportation to explore a sample of available metrics and their effectiveness in applied settings.
These metrics are described in detail in the following sections.
Bicycle Metrics
Bike Lane Miles per Road Mile According to a study of Bicycle Level of Service1, the presence of a bike lane or paved shoulder
was a significant factor in cyclists’ assessment of roadway safety, a key factor constituting
Bicycle LOS. Bike lane classes are defined in the City of Seattle GIS Bicycle Routes Data
Dictionary2 by a hierarchy including Bicycle Path, Bicycle Lane, Urban Connector, and
Neighborhood Connector. These bike lane classes are characterized by the width of the overall
outside travel lane, which includes the bike lane or shoulder width if present3. The relationship
between bike lane width and LOS can be best expressed by a weighting each bike lane according
to its width. To visually portray the different bicycle lane classes, each class was assigned a
different color in the resulting map symbology. The Bicycle Routes layer was then overlain on
top of the King County Transportation Network layer4
1 Pertrisch, T.A., Landis, B.W., Huang, H.F., McLeod, P.S., & Lamb, D. (2007). Bicycle level of service for arterials. Transportation Research Board, 34-42.
. Using the Statistics feature on each of the
respective Attribute Tables, the sum of Bicycle Lane segment lengths (in miles) of each urban
center was divided by the sum of Road segment lengths (in miles). The relative weights of each
bike lane class were not included in this calculation, because in the reviewed literature there was
no consensus on the extent to which different bike lane classes represented directly proportional
improvements in Bicycle LOS according to their width5. The two urban centers’ Bicycle LOS
2 Folsom, R. City of Seattle, Department of Transportation. (2009). Bicycle routes shapefile Seattle, WA: Retrieved from https://wagda.lib.washington.edu/data/geography/wa_cities/seattle/metadata/cd_3/metadata2009/geoguide2.htm 3Dixon, L.B. (1996). Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems. Transportation Research Board, 1538, 1-9. 4 Bui, T. King County Department of Transportation, Metro Transit Division, GIS Group. (2009). King county transportation network (TNET) Retrieved from http://www5.kingcounty.gov/sdc/Metadata.aspx?Layer=trans_network
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can then be compared using a ratio of total bike lane miles per road lane mile, creating a picture
of the distribution and availability of bicycle infrastructure within the urban center.
Average ADT (Average Daily Traffic) per Bike Lane Mile Traffic volumes have been regularly mentioned as an important factor impacting Bicycle LOS.5
Generally speaking, LOS literature has indicated an inverse relationship between traffic volumes
and Bicycle LOS6, as high traffic volumes impede bicyclists’ sense of safety in the traffic
environment. 2006 ADT (Average Daily Traffic) data from SDOT7
5 Phillips, R.G. and Guttenplan, M. (2003). Center for Urban Transportation Research. A review of approaches for assessing multi-modal quality of service. 6(4): 73-81.
provides each street’s
average daily vehicle volume totals by linear street segments of uneven lengths. The bike lanes
were therefore analyzed by linear segments so that each bike lane segment is assigned a single
ADT value. It was determined that the most useful, easily transferrable linear metric would be to
record the length of each bike lane segment in miles, as most roadway infrastructure is measured
in miles. The bike route shapefile was edited to incorporate ADT by adding a field for ADT and
adding attribute data based on the SDOT ADT information. After selecting all bike lanes with
their assigned ADT value, a metric of “Average ADT per Bike Lane Mile” was created by
aggregating the total ADT values for all bike lane segments and dividing this sum by the
aggregate length (in miles) of all bike lanes in the urban center. The resulting values for the
average ADT per bike lane mile roughly corresponds to the average traffic levels for each street
containing bicycle infrastructure, a key factor of Bicycle LOS.
6 Sprinkle Consulting. (2007). Bicycle Level of Service Applied Model, pp. 1-9. In this model, increasing the bike lane width from zero (baseline) to three feet resulted in a 10% improvement in the Bicycle LOS (p. 6). Widening from zero to five feet increased the LOS by 18%. Future applications of this research could take these metrics into account. 7 Crunican, G, & Wentz, W.M. City of Seattle, Department of Transportation. (2006). Traffic flow map.
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Average Vehicle Speed Limit per Bike Lane Mile Vehicle speed limits1,3,6 have also been indicated to be an important factor in assessing Bicycle
LOS, though significantly less so than ADT totals8
Pedestrian Metrics
. Using speed limit data from the King County
Transportation Network layer, each bike lane segment was assigned a single speed limit. Similar
to vehicle traffic volumes in the previous exercise, an aggregate statistic of average speed limit
per bike lane mile was calculated for each urban center.
Diversity of Land Uses in the Pedestrian Environment One crucial step in determining Pedestrian LOS is the identification of land use concentrations
that have a high potential to generate pedestrian travel. Using Anne Moudon’s “Targeting
Pedestrian Infrastructure Improvements”9
8 According to Pertrisch, Landis, et al (2006), 58 study participants considered Traffic Volumes to be the most significant factor of Bicycle LOS, compared to 17 who thought Traffic Speed was most important (p. 17).
as a guide for our methodology, parcels within each
urban center were selected and grouped into pedestrian-friendly land use clusters based on their
potential to generate pedestrian trips. Rather than use Moudon’s method of using aerial
photography to identify pedestrian-friendly land use clusters, we selected a less time-intensive
and sophisticated method was using each parcel’s current land use data and selecting land uses
identified by Moudon as pedestrian-friendly. Identical to Moudon’s analysis, we selected parcels
that corresponded to medium and high-density residential development, neighborhood retail and
services, and school campuses9. “Neighborhood retail and services” are identified as “retail
stores that cater to daily shopping needs – supermarkets, drugstores, restaurants, cafes, video
stores, dry cleaners, hair and barber shops, and hardware stores – as components of a commercial
9 Vernez-Moudon, A. (2001). Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel (Research Report T1803, Task 11: “Pedestrian Infrastructure”). Seattle, WA: Washington State Department of Transportation, p.48.
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center that can support walking trips.” 10 The first step of this analysis involved joining the King
County Assessor’s data11 with the City of Seattle parcel data. This step is necessary because for
pedestrian-friendly land use clusters to be populated, it was necessary to know each parcel’s
current land use as well as zoning designation, information only available in the Assessor’s table.
This point was further articulated in our interview with Chad Lynch of SDOT12. We then
constructed a hierarchy of pedestrian-friendly land uses identified in the related literature as
being generators of pedestrian trips. These land uses include medium and high-density multi-
family residential, mixed-use development, school campuses, grocery stores, neighborhood retail
services, and post offices. Other literature13
10 Vernez-Moudon, A, Hess, P, Snyder, M.C., & Stanilov, K. Washington State Transportation Center, (1997). Effects of site design on pedestrian travel in mixed-use, medium-density environments (T99034, Task 65). Seattle, WA: Washington State Department of Transportation, pp. 16-24.
identified libraries, community centers, churches,
and playgrounds as pedestrian-friendly land uses, although we did not include these land uses
because Moudon’s analysis, which is the most similar to our own, did not include them. Once the
parcels meeting pedestrian-friendly criteria were selected, we exported the pedestrian-friendly
parcels and rasterized them using the current land use designation as the associated data for each
raster cell. Doing so enabled us to analyze pedestrian-friendly land uses within the urban centers
as patches of land in FRAGSTATS. Our next step was to use the Simpson’s Diversity Index
(SIDI) function through FRAGSTATS, which gave a more accurate picture of the diversity of
each urban center’s pedestrian-friendly land uses. SIDI is a measurement of the probability that
two raster cells randomly selected from a sample will be of the same patch type, or land use in
this case, and is valued between zero to one. A greater value of SIDI (approaching a score of
one) means the urban center has a greater number of different land uses and the proportional
11 King County Department of Assessments, (2009). King county parcel record Retrieved from http://info.kingcounty.gov/assessor/DataDownload/default.aspx 12 Lynch, Chad. (2009, November 9 ). GIS Supervisor, City of Seattle Department of Transportation. Interview. 13 Cervero, R, & Duncan, M. (2003). Walking, bicycling, and urban landscapes: evidence from the san francisco bay area. American Journal of Public Health, 93(9), Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447996/
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distribution of those land uses becomes more equitable. Diversity of pedestrian-friendly land use
types is frequently mentioned as a key factor in determining pedestrian LOS10.
Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per Acre Our next metric allowed us to assess how accessible to residents each pedestrian-friendly land
use cluster was within its respective urban center. Creating a ¼ mile buffer14
14 The standard distance cited throughout the literature as the average pedestrians were willing to walk to seek commercial services or transit.
around the
pedestrian-friendly land use cluster showed the number of living units that are within walking
distance of the pedestrian-friendly cluster. To capture all of the residential parcels within the
buffer, the King County Assessor’s tables for residential parcels, including Apartment Complex,
Condo Complex and Units, and Residential Building, were joined with the Seattle Parcel
shapefile. This enabled a selection of parcels intersecting with the buffer and a calculation of the
total living units within the selected parcels. Having this information in GIS also enabled a
classification of living units per parcel, as shown in the corresponding map in the Appendix,
which provides visual identification of the urban form and spatial distribution of living units.
Calculating the density of total living units within walking distance of the cluster to total living
units in the urban center was a useful metric because it provides a measure to contrast the
residential densities of each urban center. While diversity of land uses within each pedestrian
cluster, as measured in the previous metric, is important, the pedestrian-friendly clusters are of
little use to the surrounding urban center if few residents are located within walking distance and
the cluster has effectively little pedestrian service area.9
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Average Vehicle Speed per Sidewalk Mile As most streets within each of our urban centers have abundant sidewalk coverage, the vehicle
traffic volumes were determined to have less significance for pedestrian safety than for bicycle
safety, which a previous bicycle metric addressed. Vehicle Speed, however, has been repeatedly
studied as a major factor related to pedestrian safety and the efficacy of pedestrian infrastructure
improvements.15
Transit Metrics
We intersected the vehicle speed limits data provided by King County with the
City of Seattle sidewalks layer to create a Field Statistics mean speed limit for all street segments
with sidewalks. This provided a good comparison of an important pedestrian safety factor
between each urban center.
Number of Living Units within ¼ mile of a Transit Stop per Square Mile The presence of a transit stop near one’s origin and destination is an important factor to whether
an individual will use transit or a personal automobile. This measure of availability assesses how
easily potential passenger can use transit for various kinds of trips.16 This metric will provide the
number of living units within walking distance (one quarter mile17
15 Landis, B.W., Vattikuti, V.R., Ottenberg, R.M., et.al. (2007). Modeling the Roadside Walking Environment: A Pedestrian Level of Service (Transportation Research Board Paper 01-0511). Lutz, FL: Sprinkle Consulting.
) to a transit stop per square
mile in each urban center. This metric was calculated using the same methodology as the
Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per Acre
metric, except the buffer polygon was a different shape, as it was produced by creating a one
16 Kittelson & Associates, Transit Cooperative Research Program, United States, Transit Development Corporation, and National Research Council (U.S.). 2003. Transit capacity and quality of service manual. Washington, D.C.: Transportation Research Board. Pg. 3-3. 17 “Although there is some variation between cities and income groups among the studies represented in the exhibit, it can be seen that most passengers (75 to 80% on average) walk one-quarter mile (400 meters) or less to bus stops. At an average walking speed of 3 mph (5 km/h), this is equivalent to a maximum walking time of 5 minutes.” (Transit capacity and quality of service manual, 2003).
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quarter mile buffer around all transit stops in the urban center. This metric will measure the
residential density within walking distance to transit.
Average Service Frequency per Route How frequent transit service is provided during the day is an important factor in one’s decision to
use transit.18
Average Service Span per Route
The more frequent the service is provided the shorter the wait time for a rider. This
allows more flexibility for customer in his or her trip planning. This metric will measure average
service frequency by route using the following times of day: weekday AM peak, PM peak, mid-
day, evening and night and weekend. Each route serving the urban center (meaning it has a bus
stop inside the urban center boundary) was edited by adding fields for frequency by time of day.
These frequencies were averaged for each route and a total average for the entire urban center
was calculated. This measurement indicates one level of transit service metric for each urban
center, often referred to as headways.
How long during each day that transit service is provided is also an important factor in one’s
option to using transit as opposed to other modes of transportation.19
18 Ibid, pg. 3-16.
If transit service is not
provided during certain times of the day when people want to ride, transit will not be an option
for them. Thus, increasing the number of hours that service is provided will increase the potential
number of trips taken using transit. Each route serving the urban center was edited by adding
fields for service span, both weekday and weekend. The two spans were averaged for each route
and a total average for the entire urban center was calculated. This metric will measure the
average service span, or hours of day each route provides service to the urban center, per route.
19 Ibid.
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Analysis and Interpretation of Results Our analysis involved calculating each metric for the Northgate and Capitol Hill urban centers.
We then compared and contrasted the two urban centers based on their multi-modal
transportation environments. One potential further effort to ground-truth the results of our
analysis would be to validate our metrics by comparing our findings to drive-alone rates and
other transportation behavior, as reported by the U.S. Census. We will also compare our findings
to personal observations each urban center. First, it is important to identify limiting factors that
may have impacted the quality of our analysis. We will also reflect on our analysis and its
application to future research in transportation and land use planning.
Analysis Using the metrics described in the previous section, our research included calculating each
metric within the Northgate and Capitol Hill Urban Centers. Table 1 below summarizes the
results for the nine metrics studied. A discussion of results of each metric will be included in the
following sections.
Table 1: Summary of Metrics Mode Metric Northgate Capitol Hill
Bike Bike Lane Miles per Road Lane Mile 0.11 0.10 Bike Average ADT per Bike Lane Mile 9709.07 11098.60 Bike Average Vehicle Speed Limit per Bike Lane Mile 29.04 29.23 Walk Diversity of Land Uses in Pedestrian Environment 0.7894 0.7958
Walk Number of Living Units within ¼ mile of the Pedestrian Friendly Land Use Cluster per acre 12.17 30.04
Walk Average Vehicle Speed per Sidewalk Mile 27.77 27.11
Transit Number of Living Units Within ¼ mile of a Transit Stop per Acre 10.31 28.54
Transit Average Service Frequency per Route 33.27 27.68 Transit Average Service Span per Route 15.00 15.50
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Bike Overall, the three metrics used to calculate LOS for the bicycle network produced similar results
between the two urban centers. The bike lane miles per road lane mile metric, intended to
calculate the general availability of bicycle lane infrastructure in each urban center, produced
very similar results, with 0.11 in Northgate and 0.10 in Capitol Hill. Although Capitol Hill
contains 3.9 more bike lane miles than Northgate, it also has 39 more road lane miles. This large
difference is due to the density of the street grid in Capitol Hill, including shorter blocks and
more street network connections. One problem with this metric is it does not account for the
quality of the bike lane (see Methods section for the reasons for not including this in element in
the calculation). It also does not account for the option of riding a bike on a road without a bike
lane. The average ADT per bike lane mile metric shows over 1,000 more cars on the road in
Capitol Hill compared to Northgate. Although this indicates busier streets in Capitol Hill,
presenting more opportunities for accidents, it does not account for the difference in number of
activity centers that create bicycle trips. Finally, the average speed limit per bike lane mile also
produced very similar results, with each urban center having an average of approximately 29
miles per hour for vehicles on roads that contain bike infrastructure. This is probably due to the
fact that bike lanes are often cited on roads with lower speed limits to ensure safety of the rider.
A better metric for vehicle speed would be to measure the actual speed traveled by vehicles, not
the posted speed limit. This would require more time and resources, but would show streets that
suffer from speeding vehicles that create dangerous situations for bicyclists.
Walk Two of the three metrics used to measure LOS for pedestrian infrastructure produced very
similar results and one produced a large difference. The first metric, diversity of land uses in the
pedestrian environment used SIDI to measure the diversity and distribution of pedestrian friendly
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land uses in the two urban centers. Using FRAGSTATS to calculate this metric, the results show
similar scores, with 0.7894 in Northgate and 0.7958 in Capitol Hill. This is an interesting result,
as Capitol Hill clearly contains a larger and more robust framework of pedestrian friendly land
uses. This similar scoring presents issues with using SIDI to measure land use diversity on the
urban center scale. This calculation is showing that although Northgate contains a smaller land
use cluster, it is equally diverse and distributed when compared to Capitol Hill. Perhaps a better
metric to calculate the difference in pedestrian friendly land uses would be a combination of
diversity, lot size, sidewalk width, and street connectivity. As mentioned in the Methodology
section, this study was limited from pursing these other metrics, but they could provide options
for future research. A second metric, number of living units within ¼ mile of the pedestrian
friendly land use cluster per acre, resulted in a large difference between the two urban centers.
Capitol Hill resulted in 30.04 living units per acre as opposed to Northgate with 12.17. This
clearly shows the difference in residential density between the two study areas. This metric
provides a valuable indicator to assess the residential population that can access each pedestrian
friendly land use. When viewing the distribution of the residential populations, (see Appendix A
for maps) it is clear that Capitol Hill’s residential population is distributed throughout the urban
center and not in a donut shape like Northgate. Finally, the average vehicle speed per sidewalk
mile metric produced very similar results between the two urban centers, both with an average of
27 miles per hour along streets with sidewalks. Similar to the average speed limit along bike
lanes metric, a better metric for vehicle speed along sidewalks would be to measure the actual
speed traveled by vehicles, not the posted speed limit.
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Transit Two of the three transit metrics used to measure LOS produced different results and one
produced very similar results. The number of living units within ¼ mile of a transit stop per acre
metric showed a large difference in residential density around transit service, with Northgate
having 10.31 living units per acre and Capitol Hill having 28.54 living units per acre. Similar to
the residential density metric for the walk mode, this metric shows that Capitol Hill has many
more people living within walking distance of a transit stop, which means these people will be
more likely to use transit as a mode of transportation. The second metric, average service
frequency per route, resulted in a minor difference in transit service between the two urban
centers, with Northgate having an average frequency of 33.27 minutes and Capitol Hill having an
average frequency of 27.68 minutes. The analysis of transit frequency could be sharpened in
future research by segmenting the Average Frequency per Transit Route metric into morning and
evening peak shifts, when higher transit frequencies are in greater demand by commuters. A
more focused analysis could also be performed on the Average Span per Transit Route metric by
comparing weekday and weekend transit spans in each urban center. New transit developments,
such as Sound Transit’s newly-constructed Central Link Light Rail and numerous bus rapid
transit lines have not been included in this project due to the lack of available data. Future
research on multi-modal level of service should take these new pieces of infrastructure into
account when analyzing Transit Level of Service in their respective urban centers.
Limitations Several limitations to the metrics used in this project have been identified. First, there was
conflicting literature regarding which metric was most applicable to each mode of transportation.
For instance, the definition of what constituted a “pedestrian-friendly land use” was identified in
Rowe/Perlmutter – Multi-Modal Level of Service 16
Moudon’s work, which was selected as most applicable to this project. However, the
transportation planning field has many other works that identify slightly different applicable land
uses. Asserting with more certainty what constitutes a pedestrian-friendly land use, perhaps by
developing an independent metric to assess a parcel’s pedestrian-friendliness through measuring
the number of trips it generates, is necessary to make our “Diversity of Land Uses in the
Pedestrian Environment” more useful. Transferability of the data also represents a potential
problem area. While it was acceptable in this project to compare multi-modal LOS analyses for
different urban centers within the City of Seattle, making similar comparisons between urban and
suburban areas is more problematic because of the fundamentally different characteristics of the
built environment in these areas. The quality and availability of data was also a limiting factor.
Width of sidewalks and the presence of parked cars were widely identified in the pedestrian LOS
literature as important factors, yet we could not find high-quality data to perform these metrics.
Finally, the time and resources allotted to complete this project limited the complexity of the
analysis we could perform.
Implications This analysis of multi-modal level of service has many applications in the transportation
planning, real estate, and energy sectors. In transportation planning, public transit service
allocation and upgrades could be determined by examining neighborhoods’ transit LOS and
using one of the metrics identified in this project in assessing which area has the greatest need
for new infrastructure. In the real estate development sector, the design guidelines for parking
requirements in new buildings could potentially be linked to the availability of multi-modal
infrastructure in the immediate vicinity. This could mitigate the problem of over-provision of
parking spaces in new medium and high-density multi-family housing, a factor that has been
Rowe/Perlmutter – Multi-Modal Level of Service 17
identified as contributing to lack of housing affordability in new developments. More broadly, in
the energy sector, it has been widely documented that one important step to reducing greenhouse
gas emissions and curbing single-occupancy vehicle trips is by improving the availability of
multi-modal transportation infrastructure.
In addition to these planning applications, multi-modal LOS has been identified as a
supplemental metric to evaluating transportation concurrency under Washington’s Growth
Management Act (GMA). Currently, roadway LOS, generally roadway capacity, is used as a
metric to determine if transportation infrastructure is adequate to accommodate new trips
generated by proposed new development. While appropriate for some communities, this roadway
concurrency metric often suggests improvements to accommodate more vehicle capacity, not
multi-modal capacity. Communities that have existing multi-modal capacity could benefit from
concurrency metrics that measure transit, bike, and walking facilities. This analysis could use the
GMA transportation concurrency law to foster smart growth in urban centers and potentially help
fund multi-modal infrastructure improvements. Multi-modal LOS provides a new approach to
measuring transportation infrastructure. This measurement will be critical to plan and allocate
resources as our urban centers prepare to accommodate new growth and provide sustainable
transportation solutions.
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Appendix A: Project Maps
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Appendix B: Data Dictionary Name Description Type Geometry Coordinate
System Source
Bike 1. Bicycle
Routes
Location of bike lanes in the City of Seattle
Shapefile Polyline Washington State Plane, North Zone
WAGDA – City of Seattle
2. Street Arterials
Location of arterial streets in the City of Seattle
Shapefile Polyline Washington State Plane, North Zone
WAGDA – City of Seattle
3. ADT totals for street segments
Traffic Flow Map: ADT (Average Daily Traffic) totals for arterial streets in the City of Seattle
PDF N/A N/A City of Seattle – SDOT7
4. Speed Limits Speed Limits of street segments in King County, WA
Shapefile Polyline Washington State Plane, North Zone
WAGDA – King County Department of Transportation, Metro Transit, GIS Group
5. Urban Center boundaries
Urban Center boundaries delineated by Puget Sound Regional Council (PSRC)
Shapefile Polygon Washington State Plane, North Zone
WAGDA – City of Seattle
Walk 1. Sidewalks Location of
sidewalks in the City of Seattle
Shapefile Polyline Washington State Plane, North Zone
WAGDA – City of Seattle
2. King County Parcel Record
Parcel data listings of current land uses for King County, WA
Attribute table
Database file
N/A King County Assessor’s Office11
3. Parcels – City of Seattle
Parcel data listing current land uses in the City of Seattle
Shapefile Polygon Washington State Plane, North Zone
WAGDA – City of Seattle
4. Speed Limits Speed Limits of street segments in King County, WA
Shapefile Polyline Washington State Plane, North Zone
WAGDA – King County Department of Transportation, Metro Transit, GIS Group
5. Residential Living Units
Parcel data listing number of living units in each
Attribute table
Database file
N/A King County Assessor’s Office11
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residential parcel in King County, WA
6. Urban Center boundaries
Urban Center boundaries delineated by Puget Sound Regional Council (PSRC)
Shapefile Polygon Washington State Plane, North Zone
WAGDA – City of Seattle
Transit 1. Transit
Routes Transit routes in King County, WA
Shapefile Polyline Washington State Plane, North Zone
WAGDA – King County Department of Transportation, Metro Transit, GIS Group
2. Transit Stops Transit stop locations in King County, WA
Shapefile Polygon Washington State Plane, North Zone
WAGDA – King County Department of Transportation, Metro Transit, GIS Group
3. King County Parcel Record
Parcel data listings of current land uses for King County, WA
Attribute table
Database file
N/A King County Assessor’s Office11
4. Transit Route Frequency Data
Frequency of transit service in King County, WA
Attribute table
Database file
N/A King County Department of Transportation, Metro Transit
5. Transit Route Span Data
Span of transit service in King County, WA
Attribute table
Database file
N/A King County Department of Transportation, Metro Transit
7. Urban Center boundaries
Urban Center boundaries delineated by Puget Sound Regional Council (PSRC)
Shapefile Polygon Washington State Plane, North Zone
WAGDA – City of Seattle
Rowe/Perlmutter – Multi-Modal Level of Service 36
Works Cited Bui, T. King County Department of Transportation, Metro Transit Division, GIS Group. (2009).
King county transportation network (TNET) Retrieved from http://www5.kingcounty.gov/sdc/Metadata.aspx?Layer=trans_network
Cervero, R, & Duncan, M. (2003). Walking, bicycling, and urban landscapes: evidence from the San Francisco bay area. American Journal of Public Health, 93(9), Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447996/
Crunican, G, & Wentz, W.M. City of Seattle, Department of Transportation. (2006). Traffic flow map.
Dixon, L.B. (1996). Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems. Transportation Research Board, 1538, 1-9.
Folsom, R. City of Seattle, Department of Transportation. (2009). Bicycle routes shapefile Seattle, WA: Retrieved from https://wagda.lib.washington.edu/data/geography/wa_cities/seattle/metadata/cd_3/metadata2009/geoguide2.htm
King County Department of Assessments, (2009). King county parcel record Retrieved from http://info.kingcounty.gov/assessor/DataDownload/default.aspx
Kittelson & Associates, Transit Cooperative Research Program, United States, Transit Development Corporation, and National Research Council (U.S.). 2003. Transit capacity and quality of service manual. Washington, D.C.: Transportation Research Board. Pg. 3-3.
Landis, B.W., Vattikuti, V.R., Ottenberg, R.M., et.al. (2007). Modeling the Roadside Walking Environment: A Pedestrian Level of Service (Transportation Research Board Paper 01-0511). Lutz, FL: Sprinkle Consulting.
Lynch, Chad. (2009, November 9). GIS Supervisor, City of Seattle Department of Transportation. Interview.
Pertrisch, T.A., Landis, B.W., Huang, H.F., McLeod, P.S., & Lamb, D. (2007). Bicycle level of service for arterials. Transportation Research Board, 34-42.
Phillips, R.G. and Guttenplan, M. (2003). Center for Urban Transportation Research. A review of approaches for assessing multi-modal quality of service. 6(4): 73-81.
Sprinkle Consulting. (2007). Bicycle Level of Service Applied Model, pp. 1-9.
Vernez-Moudon, A, Hess, P, Snyder, M.C., & Stanilov, K. Washington State Transportation Center, (1997). Effects of site design on pedestrian travel in mixed-use, medium-density environments (T99034, Task 65). Seattle, WA: Washington State Department of Transportation.
Vernez-Moudon, A. (2001). Targeting Pedestrian Infrastructure Improvements: A Methodology to Assist Providers in Identifying Suburban Locations with Potential Increases in Pedestrian Travel (Research Report T1803, Task 11: “Pedestrian Infrastructure”). Seattle, WA: Washington State Department of Transportation.
Rowe/Perlmutter – Multi-Modal Level of Service 37