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August 7, 2003 Virtual City: A Heterogeneous System Model of an Intelligent Road Navigation System Incorporating Data Mining Concepts Okyere Indiana University - Bloomington Mentors: Professor Edward A. Lee Yang Zhao http://chess.eecs.berkeley.edu WORK R oute A R oute C R oute D R oute B 55 M iles 45 M iles 60 M iles 65 M iles HOME 40 M iles 60 M iles 50 M iles 35 M iles Speed Lim it 45m ph Speed Lim it 65m ph Speed Lim it 50m ph Highw ay Speed Lim it 70m ph Highway Speed Lim it 65m ph Speed Lim it 45m ph Speed Lim it 45 m ph Speed Lim it 50m ph Virtual City StaticRoad M ap WORK R oute A R oute C R oute D R oute B 55 M iles 45 M iles 60 M iles 65 M iles HOME 40 M iles 60 M iles 50 M iles 35 M iles Speed Lim it 45m ph - In-C ity Traffic - Speed R eduction 15% Speed Lim it 65m ph - R oad C onstruction - Speed R eduction 15% Speed Lim it 50m ph - Speed R eduction 0% Highway Speed Lim it 70m ph - R ush H ourTraffic - Speed R eduction 20% Highway Speed Lim it 65m ph - Speed R eduction 0% Speed Lim it 45m ph - LightTraffic - Speed R eduction 10% Speed Lim it 45 m ph - Intersection Traffic - Speed R eduction 20% Speed Lim it 50m ph - R esidential Area - Speed R eduction 5% Virtual City Real-Tim eRoad M ap 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 Map Static Road Map A I C J B K D H G F E P O M L Q N StreetM ap 1 5 10 15 323 sq.ft. 511 sq.ft. 835 sq.ft. City A C ity B City C 210 sq.ft. City D START END A I C J B K D H G F E P O M L Q N StreetM ap 1 5 10 15 323 sq.ft. 511 sq.ft. 835 sq.ft. C ity A C ity B C ity C 210 sq.ft. C ity D START END Dynamic Road Map Dynamic 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 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. 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 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
