Dynamic vehicle routing using Ant Based Control

1. Dynamic vehicle routingusing Ant Based Control Ronald Kroon Leon Rothkrantz Delft University of Technology October 2, 2002 Delft

2. Contents • Introduction • Theory • Ant Based Control • Simulation environment and Routing system • Experiment and results • Conclusions and recommendations

3. Introduction (1) Dynamic vehicle routing using Ant Based Control: • Routing cars through a city • Using dynamic data • Using an Ant Based Control algorithm

4. Introduction (2) Goals: • Design and implement a prototype of dynamic Routing system using Ant Based Control • Design and implement a simulation environment for traffic • Test Routing system

5. Introduction (3) • Navigate a driver through a city • Find the closest parking lot • Divert from congestions Possible applications:

8. Schematic overview of the PITA components

9. 3D Model of dynamic traffic data

10. Theory (1) Natural ants find the shortest route

11. Theory (2) Choosing randomly

12. Theory (3) Laying pheromone

13. Theory (4) Biased choosing

14. Theory (5) 3 reasons for choosing the shortest path: • Earlier pheromone (trail completed earlier) • More pheromone (higher ant density) • Younger pheromone (less diffusion)

15. Ant Based Control (1) Application of ant behaviourin network management • Mobile agents • Probability tables • Different pheromone for every destination

16. 3 2 1 6 4 5 7 Ant Based Control (2) Probability table

17. Ant Based Control (3) Forward agents • 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

18. Ant Based Control (4) Backward agents • Move back from destination to source • Use reverse path of forward agent • Update the probabilities for going to this destination

19. Ant Based Control (5) Updating probabilities • 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

20. Simulation GPS-satellite Vehicle Routing system Simulation environment and Routing system (1) Architecture

21. GPS-satellite • Position • determination Vehicle • Routing • Dynamic data Routing system Simulation environment and Routing system (2) Communication flow

22. Routing system Route finding system Timetable updating system Dynamic data Routing Memory Routing system (1)

23. 3 2 1 6 4 5 7 Routing system (2) Timetable

24. t2 20 3 2 1 6 4 5 7 Routing system (3) Update information t1

26. Simulation environment (1) Map of Beverwijk

27. Simulation environment (2) Map representation for simulation

28. Simulation environment (3) Simulation with driving vehicles

29. Simulation environment (4) Features • Traffic lights • Roundabouts • One-way traffic • Number of lanes • High / low priority roads • Precedence rules • Speed variation per road • Traffic distribution • Road disabling

30. Experiment

31. Results • 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):

32. Conclusions • 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

33. Recommendations • 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

34. Start demo Demo