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Mediamatics / Knowledge based systems
Dynamic vehicle routingDynamic vehicle routingusing Ant Based Controlusing Ant Based Control
Ronald Kroon
Leon Rothkrantz
Delft University of Technology
October 2, 2002
Delft
Mediamatics / Knowledge based systems 2
ContentsContents
IntroductionTheoryAnt Based ControlSimulation environment and Routing systemExperiment and resultsConclusions and recommendations
Mediamatics / Knowledge based systems 3
Introduction (1)Introduction (1)
Dynamic vehicle routing
using Ant Based Control:
Routing cars through a city Using dynamic data Using an Ant Based Control algorithm
Mediamatics / Knowledge based systems 4
Introduction (2)Introduction (2)
Design and implement a prototype of dynamic Routing system using Ant Based Control
Design and implement a simulation environment for traffic
Test Routing system
Goals:
Mediamatics / Knowledge based systems 5
Introduction (3)Introduction (3)
Navigate a driver through a city Find the closest parking lot Divert from congestions
Possible applications:
Mediamatics / Knowledge based systems 8
Schematic overview of the PITA Schematic overview of the PITA componentscomponents
Mediamatics / Knowledge based systems 9
3D Model of dynamic traffic data3D Model of dynamic traffic data
Mediamatics / Knowledge based systems 14
Theory (5)Theory (5)
Earlier pheromone (trail completed earlier) More pheromone (higher ant density) Younger pheromone (less diffusion)
3 reasons for choosing the shortest path:
Mediamatics / Knowledge based systems 15
Mobile agents Probability tables Different pheromone for every destination
Ant Based Control (1)Ant Based Control (1)
Application of ant behaviourin network management
Mediamatics / Knowledge based systems 16
Ant Based Control (2)Ant Based Control (2)
(Node 2) Next 1 3 5
Destination
1 0.90 0.02 0.08
3 0.03 0.90 0.07
4 0.44 0.19 0.37
5 0.08 0.05 0.87
… … … …
Probability table
13
2
4 5
6
7
Mediamatics / Knowledge based systems 17
Generated regularly from every node with random destination
Choose route according to a probability Probability represents strength of pheromone
trail Collect travel times and delays
Ant Based Control (3)Ant Based Control (3)
Forward agents
Mediamatics / Knowledge based systems 18
Move back from destination to source Use reverse path of forward agent Update the probabilities for going to this
destination
Ant Based Control (4)Ant Based Control (4)
Backward agents
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Probability for choosing the node the forward agent chose is incremented
Depends on:• Sum of collected travel times• Delay on this path
Update formula: Δp = A / t + B
Probabilities for choosing other nodes are slightly decremented
Ant Based Control (5)Ant Based Control (5)
Updating probabilities
Mediamatics / Knowledge based systems 20
Simulation environment and Simulation environment and Routing system (1)Routing system (1)
Architecture
GPS-satellite
Vehicle
Routing system
Simulation
Mediamatics / Knowledge based systems 21
GPS-satellite
Vehicle
Routing system
• Position determination
• Routing
• Dynamic data
Simulation environment and Simulation environment and Routing system (2)Routing system (2)
Communication flow
Mediamatics / Knowledge based systems 22
Routing system (1)Routing system (1)
Routing system
Route finding system
MemoryTimetable updating system
Dynamic data
Routing
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1 2 4 5 …
1 - 12 15 - …
2 11 - - 18 …
4 14 - - 13 …
5 - 18 14 - …
… … … … … …
13
2
4 5
6
7
Routing system (2)Routing system (2)Timetable
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Routing system (3)Routing system (3)
Update information
13
2
4 5
6
7
t1
t220
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The effect of new information on an entry in the timetable
02468
10121416182022
time
tim
etab
le v
alu
e
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Simulation environment (1)Simulation environment (1)
Map of Beverwijk
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Simulation environment (2)Simulation environment (2)
Map representation for simulation
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Simulation environment (3)Simulation environment (3)
Simulation
with driving
vehicles
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Traffic lights Roundabouts One-way traffic Number of lanes High / low priority roads
Simulation environment (4)Simulation environment (4)
Features
Precedence rules Speed variation per road Traffic distribution Road disabling
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ResultsResults
32 % profit for all vehicles, when some of them are guided by the Routing system
19 % extra profit for vehicles using the Routing system
In this test case (no realistic environment):
Mediamatics / Knowledge based systems 32
ConclusionsConclusions
Successful creation of Routing system and simulation environment
Test results:– Routing system is effective:
Smart vehicles take shorter routes Other vehicles also benefit from better
distribution of traffic
– Routing system adapts to new situations: 15 sec – 2 min
Mediamatics / Knowledge based systems 33
RecommendationsRecommendations
Let vehicle speed depend on saturation of the road
Update probabilities using earlier found routes compared to new route
Use the same pheromone for all parkings near a city center