Dynamic routing versus static routing

1. Dynamic routing versus static routing Prof. drs. Dr. Leon Rothkrantz http://www.mmi.tudelft.nl http://www.kbs.twi.tudelft.nl

2. Outline presentation • Problem definition • Static routing Dijkstra shortest path algorithm • Dynamic traffic data (historical data, real time data) • Dynamic routing using 3D-Dijkstra algorithm • Travel speed prediction using ANN • Personal intelligent traveling assistant (PITA) • PITA in cars and in trains

3. Introduction Problem definition • Find the shortest/fastest route from A to B using dynamic route information. • Research if dynamic routing results in shorter traveling time compared to shortest path • Is it possible to route a traveler on his route in dynamically changing environments ?

4. (Non-) congested road

5. Traffic

6. Testbed: graph of highways

7. MONICA networkMany sensors/wires along the road to measure the speed of the cars

8. Smart Road • Many sensors (smart sensors) along a road • Sensor devices set up a wireless ad-hoc network • Sensor in the car is able to communicate with the road • Congestion, icy roads can be detected by the sensors and communicated along the network, to inform drivers remote in place and time • GPS, GSM can be included in the sensornetworks • Wireless communication by wired lamppost/streetlights

10. Real speed on a road segment during peak hour

11. 3 dimensional graphUse 3D Dijkstra

12. Why not search in this 3 dim. graph ? • This will become a giant graph: - constructing such a 3 dimensional graph (estimating travel times) would take too much time - performance of shortest path algorithm for such a graph will be very poor

13. Shortest path via dynamic routing

14. Expert systemBased on knowledege/experience of daily cardriver • Translate routes to trajectories between junctions and assign labels entrance, route, file and exit to each trajectory (entrance kleinpolderplein ypenburg) (route ypenburg prins_claus) (file prins_claus badhoevedorp) (route badhoevedorp nieuwe_meer) (exit nieuwe_meer coenplein)

15. Design (1)

16. Schematic overview of a P+R route.

17. Design (2)

18. Static car and public transport routes

19. Dynamic car route

20. P+R route

21. Expert system • Translate routes to trajectories between junctions and assign labels entrance, route, file and exit to each trajectory (entrance kleinpolderplein ypenburg) (route ypenburg prins_claus) (file prins_claus badhoevedorp) (route badhoevedorp nieuwe_meer) (exit nieuwe_meer coenplein)

22. Example alternative routesusing expert knowledge

23. Implementation in CLIPS

24. Results of dynamic routing • Based on historical traffic speed data dynamic routing is able to save approximately 15% of travel time • During special incidents (accidents, road work,…) savings in travel time increases • During peak hours savings decreases

25. User preferences • Shortest travel time • Preference routing via highways, secondary roads minimized • Preferred routing (not) via toll routes • Fastest route or shortest route • Route with minimal of traffic jams

26. Traffic Current systems developed at TUDelft • Prediction of travel time using ANN (trained on historical data) • Model of speed as function of time average over road segments/trajectories • Static routing using Dijkstra algorithm • Dynamic routing using 3D Dijkstra • Dynamic routing using Ant Based Control algorithm • Personal Traveling Assistant online end of 2008

27. NN Classifiers • Feed-Forward BP Network • single-frame input • two hidden layers • logistic output function in hidden and output layers • full connections between layers • single output neuron

28. NN Classifiers (continued) • Time Delayed Neural Network • multiple frames input • coupled weights in first hidden layer for time-dependency learning • logistic outputfunction in hidden and output layers

29. NN Classifiers (continued) • Jordan RecursiveNeural Network • single frame input • one hidden layer • logistic output functionin hidden and output layer • context neuron for time-dependency learning

30. Factors which have impact on the speed Factors • Time • Day of the week • Month • Weather • Special events

31. Impact on speed Time

32. Impact on speed Day of the week

33. Impact on speed Day of the week

34. Impact on speed Month

35. Impact on speed Month

36. Impact on speed Weather

37. Impact on speed Special events

38. Model 1 Is it possible to predict average speed on a special location and time?

39. Model 1

40. Model 2 Is it possible to predict average time 25 minutes ahead on a special location with an error of less then 10% ?

41. Model 2

42. Model 3

43. Test results Model 1 • 6 networks tested • Tuesday • A12 in the direction of Gouda • Best results with 5 neurons in hidden layer

44. Test results Model 1

45. Test results Model 2 • 9 networks tested • Tuesday • A12 in the direction of Gouda • Best results with 9 neurons in the hidden layer

46. Test results Model 2

47. Test results

48. Test results Results of the best performing network: • 76% of the values with difference of 10% or less • Average error is more than 20% • Deleting outliers: average error less than 9%

49. Conclusions • Existing research • Formula of Fletcher and Goss • Impact • Results

50. Current system • Model (based on historical data) • Accidents and work on the road • Travel time (based on Recurrent neural networks) • Data collection (average speed per segment, per road)