1. Mediamatics / Knowledge based systems Dynamic vehicle routing using Ant Based Control Ronald Kroon Leon Rothkrantz Delft University of Technology October 2, 2002 Delft

2. Mediamatics / Knowledge based systems2 Contents Introduction Theory Ant Based Control Simulation environment and Routing system Experiment and results Conclusions and recommendations

3. Mediamatics / Knowledge based systems3 Introduction (1) Dynamic vehicle routing using Ant Based Control: Routing cars through a city Using dynamic data Using an Ant Based Control algorithm

4. Mediamatics / Knowledge based systems4 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:

5. Mediamatics / Knowledge based systems5 Introduction (3) Navigate a driver through a city Find the closest parking lot Divert from congestions Possible applications:

6. Mediamatics / Knowledge based systems6

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8. 8 Schematic overview of the PITA components

9. Mediamatics / Knowledge based systems9 3D Model of dynamic traffic data

10. Mediamatics / Knowledge based systems10 Theory (1) Natural ants find the shortest route

11. Mediamatics / Knowledge based systems11 Theory (2) Choosing randomly

12. Mediamatics / Knowledge based systems12 Theory (3) Laying pheromone

13. Mediamatics / Knowledge based systems13 Theory (4) Biased choosing

14. Mediamatics / Knowledge based systems14 Theory (5) Earlier pheromone (trail completed earlier) More pheromone (higher ant density) Younger pheromone (less diffusion) 3 reasons for choosing the shortest path:

15. Mediamatics / Knowledge based systems15 Mobile agents Probability tables Different pheromone for every destination Ant Based Control (1) Application of ant behaviour in network management

16. Mediamatics / Knowledge based systems16 Ant Based Control (2) (Node 2)Next135 Destination 10.900.020.08 30.030.900.07 40.440.190.37 50.080.050.87 ………… Probability table 1 3 2 4 5 6 7

17. Mediamatics / Knowledge based systems17 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) Forward agents

18. Mediamatics / Knowledge based systems18 Move back from destination to source Use reverse path of forward agent Update the probabilities for going to this destination Ant Based Control (4) Backward agents

19. Mediamatics / Knowledge based systems19 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) Updating probabilities

20. Mediamatics / Knowledge based systems20 Simulation environment and Routing system (1) Architecture GPS-satellite Vehicle Routing system Simulation

21. Mediamatics / Knowledge based systems21 GPS-satellite Vehicle Routing system Position determination Routing Dynamic data Simulation environment and Routing system (2) Communication flow

22. Mediamatics / Knowledge based systems22 Routing system (1) Routing system Route finding system Memory Timetable updating system Dynamic data Routing

23. Mediamatics / Knowledge based systems23 1245… 1 -1215-… 2 11--18… 4 14--13… 5 -1814-… … …………… 1 3 2 4 5 6 7 Routing system (2) Timetable

24. Mediamatics / Knowledge based systems24 Routing system (3) Update information 1 3 2 4 5 6 7 t1t1 t2t2 20

25. Mediamatics / Knowledge based systems25

26. Mediamatics / Knowledge based systems26 Simulation environment (1) Map of Beverwijk

27. Mediamatics / Knowledge based systems27 Simulation environment (2) Map representation for simulation

28. Mediamatics / Knowledge based systems28 Simulation environment (3) Simulation with driving vehicles

29. Mediamatics / Knowledge based systems29 Traffic lights Roundabouts One-way traffic Number of lanes High / low priority roads Simulation environment (4) Features Precedence rules Speed variation per road Traffic distribution Road disabling

30. Mediamatics / Knowledge based systems30 Experiment

31. Mediamatics / Knowledge based systems31 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. Mediamatics / Knowledge based systems32 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. Mediamatics / Knowledge based systems33 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. Mediamatics / Knowledge based systems34 Start demo Demo