August 7, 2003
Virtual City: A Heterogeneous System Model of an Intelligent Road Navigation System Incorporating Data Mining Concepts
Mike Kofi OkyereIndiana University - Bloomington
Mentors:Professor Edward A. LeeYang Zhao
http://chess.eecs.berkeley.edu
WORK
Route A Route C Route DRoute B
55 Miles 45 Miles60 Miles 65 Miles
HOME
40 Miles 60 Miles 50 Miles 35 Miles
Speed Limit45mph
Speed Limit65mph
Speed Limit50mph
HighwaySpeed Limit
70mph
HighwaySpeed Limit
65mph
Speed Limit45mph
Speed Limit45 mph
Speed Limit50mph
Virtual CityStatic Road Map
WORK
Route A Route C Route DRoute B
55 Miles 45 Miles60 Miles 65 Miles
HOME
40 Miles 60 Miles 50 Miles 35 Miles
Speed Limit45mph
-In-City Traffic
-Speed Reduction
15%
Speed Limit65mph
-Road Construction
-Speed Reduction
15%
Speed Limit50mph
-Speed Reduction
0%
HighwaySpeed Limit
70mph-
Rush Hour Traffic-
Speed Reduction20%
HighwaySpeed Limit
65mph-
Speed Reduction0%
Speed Limit45mph
-Light Traffic
-Speed Reduction
10%
Speed Limit45 mph
-Intersection Traffic
-Speed Reduction
20%
Speed Limit50mph
-Residential Area
-Speed Reduction
5%
Virtual CityReal-Time Road Map
The New Approach
The Intelligent Road Navigation System consists of a dynamic data warehouse that contains real-time road information, ranging from road name and length to road inclination and traffic density. The most efficient route is calculated by an extension of the Dijkstra’s Shortest Path Algorithm to obtain driving directions that focus on minimizing either travel time, gasoline usage, driving mileage or a combination of all.
The New Approach
The Intelligent Road Navigation System consists of a dynamic data warehouse that contains real-time road information, ranging from road name and length to road inclination and traffic density. The most efficient route is calculated by an extension of the Dijkstra’s Shortest Path Algorithm to obtain driving directions that focus on minimizing either travel time, gasoline usage, driving mileage or a combination of all.
Static Road MapStatic Road MapStatic Road MapStatic Road Map
A IC JB KD HGFE POML QN
Street Map
1
5
10
15
323 sq. ft.511 sq. ft.
835 sq. ft.
City A
City B
City C210 sq. ft.
City DSTART
END
A IC JB KD HGFE POML QN
Street Map
1
5
10
15
323 sq. ft.511 sq. ft.
835 sq. ft.
City A
City B
City C210 sq. ft.
City DSTART
END
Dynamic Road MapDynamic Road MapDynamic Road MapDynamic Road Map
Advantages of the Intelligent Road Navigation
System
• Calculates routes by taking into account the various road characteristics in addition to the static data:
•Congestion & Construction Sites
•Road Inclination & Weather Condition
•Enables roadways to operate at peak volume levels
•Establishes a self-balancing Traffic System
Advantages of the Intelligent Road Navigation
System
• Calculates routes by taking into account the various road characteristics in addition to the static data:
•Congestion & Construction Sites
•Road Inclination & Weather Condition
•Enables roadways to operate at peak volume levels
•Establishes a self-balancing Traffic System
Disadvantages of Current Route Planners
•Calculation of shortest and “fastest” route using static data:
•Road Length
•Constant Travel Speed
•No emphasis is placed on traffic behavior or road specifications
Disadvantages of Current Route Planners
•Calculation of shortest and “fastest” route using static data:
•Road Length
•Constant Travel Speed
•No emphasis is placed on traffic behavior or road specifications
Overview
Generating satisfactory directions for route guidance is a challenging task, because the effectiveness and advantage of particular routes depend on various road specifications and characteristics. Current route planners, such as MapQuest.com, Yahoo.com and Maps.com, present only limited route options to the drivers of personal or commercial vehicles. Such directions are based on static evaluation criteria and do not consider real-time information related to traffic conditions, road construction, road inclination or weather conditions.
Overview
Generating satisfactory directions for route guidance is a challenging task, because the effectiveness and advantage of particular routes depend on various road specifications and characteristics. Current route planners, such as MapQuest.com, Yahoo.com and Maps.com, present only limited route options to the drivers of personal or commercial vehicles. Such directions are based on static evaluation criteria and do not consider real-time information related to traffic conditions, road construction, road inclination or weather conditions.
SELECT START, END, [LENGTH]/([SPEED]*[TrafficFactor]*[ConstructionFactor])*60 AS TrafficTime
FROM Construction INNER JOIN (Traffic INNER JOIN RoadRules ON Traffic.TrafficTypeID = RoadRules.TRAFFIC) ON Construcion.ConstructionSiteTypeID = RoadRules.CONSTRUCTION;"
IRNS Sample Run•User Input :
•Start Location: [A1]
•Destination : [P20]
•The DataReader actor executes the Query [Fig. 4] on the data warehouse and sends result sets to GraphBuilder actor
•The GraphBuilder actor converts the received result sets into a directed graph (digraph) and sends the created digraph to the ShortestPath actor
•The ShortestPath actor verifies the validity of the input nodes and graph, in order to then determine the most efficient route by applying an extension of Dijkstra’s Shortest Path Algorithm
IRNS Sample Run•User Input :
•Start Location: [A1]
•Destination : [P20]
•The DataReader actor executes the Query [Fig. 4] on the data warehouse and sends result sets to GraphBuilder actor
•The GraphBuilder actor converts the received result sets into a directed graph (digraph) and sends the created digraph to the ShortestPath actor
•The ShortestPath actor verifies the validity of the input nodes and graph, in order to then determine the most efficient route by applying an extension of Dijkstra’s Shortest Path Algorithm
Figure 1 - Showing the “Fastest” Route using a Static Data Figure 2 – Showing the Fastest Route using Dynamic Data
Figure 3 – Showing the Ptolemy II model of the Intelligent Route Navigation System
Figure 4 – Showing the Query used to find the shortest travel time between road segments
Shortest Path from (a) to (d) is calculated as follows:
Graph G = (V, E):
V = {a, b, c, d}, E = {(a, b, 4), (a, c, 2), (b, c, 3), (b, d, 1),(c, a, 2), (c, b, 1), (c, d, 5)}
1. Init: d(a) = 0, d(b) = INF., d(c) = INF., d(d) = INF.
2. 0+ [a,b] = 4 < INF. [set distance from (a) (b) = 4]
0+ [a,c] = 2 < INF. [set distance from (a) (c) = 2]
[Pick [c]]
3. 2 + [b,c] = 3 < 4 [set distance from (a) (b) = 3]
2 + [b,d] = 7 < INF. [set distance from (a) (d) = 7]
[Pick [b]]
4. 3+ [b,d] = 4 [set distance from (a) (d) = 4]
The algorithm stops, since the shortest path has been found.
Shortest Path: (a) (c) (b) (d)
Dijkstra’s Shortest Path Algorithm Dijkstra’s Shortest Path Algorithm
