Fowe Thesis Full

1,686 views
1,569 views

Published on

Road-Traffic Monitoring and Routing;
Stochastic Algorithms For Safety and Efficiency

This is my thesis slides.

Published in: Technology, Education
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,686
On SlideShare
0
From Embeds
0
Number of Embeds
168
Actions
Shares
0
Downloads
0
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

Fowe Thesis Full

  1. 1. ROAD-TRAFFIC MONITORING AND ROUTING; STOCHASTIC ALGORITHMS FOR SAFETY AND EFFICIENCY By Adeyemi Fowe Masters Degree Thesis Defence Applied Science Department (Engineering Science and Systems) ‏ University of Arkansas at Little rock. Supervisor Dr. Yupo Chan Professor and Founding Chair Systems Engineering Department University of Arkansas at Little Rock.
  2. 2. The Transportation Problem Image from: http://www.railway-technology.com/projects/bangkok/bangkok3.html The demand on Infrastructure is on the increase, building more roads will not solve the problem. Hence the need for ITS (Intelligent Transportation Systems). It involves the application of Algorithms & Mathematical Models in describing and solving day-to-day transportation problems. One of the basic need of a driver on The road is Fastest Travel Time.
  3. 3. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  4. 4. ATIS ( Advanced Traveler Information System)
  5. 5.
  6. 6. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  7. 7. Thesis Contributions <ul><li>A new non-FIFO routing algorithm ( WSDOT ) that search for possible wait-times (en-route) in a time-dependent transportation network is developed. (Fall 07) </li></ul><ul><li>A mathematical model to compute Time-dependent Incident Probabilities from historical traffic and incident data. (Fall 08) </li></ul><ul><li>An extension of WSDOT algorithm to WSDOT-R ( WSDOT-with-Risk ) algorithm which simultaneously minimizes a driver’s exposure to incident risk even as the fastest travel time is desired. (Summer 08) </li></ul><ul><li>A mathematical algorithm to estimate traffic volumes in unknown arcs using partial information from upstream/downstream neighbors and entropy maximization. (Spring 09) </li></ul><ul><li>Finally this thesis presents the application of all the models, their efficiency, and their performance on a real transportation network. Using the Highway Road of Central Arkansas as a case study, we design and present a prototype demo of a mini-TMC (Traffic Management Center). (Spring 08) </li></ul>
  8. 8. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  9. 9. Data Collection Systems <ul><li>Inductive Loop Sensors </li></ul><ul><li>Traffic/Surveillance Cameras </li></ul><ul><li>Aerial Photos </li></ul><ul><li>CFVD (Cellular Floating Vehicle Data) </li></ul><ul><li>Video Vehicle Detection Systems </li></ul>
  10. 10. Data is Key to a Functional ATIS <ul><li>Partial Information (incomplete Data) </li></ul><ul><li>Perfect Information (Complete Data) </li></ul><ul><li>Static/Persistent Data (Average & non-time dependent). </li></ul><ul><li>Dynamic Data (Time Dependent) </li></ul><ul><li>Real-Time Data (Streaming and Complete Time Dependent Data) </li></ul>Have Need
  11. 11. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  12. 12. Forecasting
  13. 13. Spatial Relationships In both spatial and temporal Dimensions; Closer by neighbors have tendency to be similar than farther away neighbors.
  14. 14. Arc Volume Estimation
  15. 15. Spatial Matrix First –Order Spatial Arc Neighbors
  16. 16. Spatial Matrix Second –Order Spatial Arc Neighbors
  17. 17. Spatial Weights
  18. 18. Spatial Weights
  19. 19. Model Formulation
  20. 20. Model Formulation
  21. 21. Model Formulation
  22. 22. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  23. 23. Arc Volume Estimate Naïve Solution
  24. 24. Entropy Maximization - Discrete
  25. 25. Entropy Maximization with Binary Search (EMBS)
  26. 26. Entropy Maximization with Binary Search (EMBS)
  27. 27. Entropy Maximization with Binary Search (EMBS)
  28. 28. Complexity Analysis
  29. 29. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  30. 30.
  31. 31.
  32. 33.
  33. 34.
  34. 35. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Sample Computation </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  35. 36. Time Dependent Incident Probability
  36. 37. Fundamental Diagram of Traffic Flow
  37. 38. Time Dependent Incident Probability – Peak vs Off-Peak
  38. 39. Poisson – Time Dependent Probability Model
  39. 40. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  40. 41. Key Terms Discrete vs Continuous representation of time A system is said to be discrete in time when the total time period T is divided into smaller periodic segments of time with integer increments. While a system is said to be continuous in time when point in the time space has a different value which can be represented by a floating decimal value of time. FIFO vs non-FIFO FIFO (First In First Out) represents a transportation network which follows; early departure  assured early arrival. While non-FIFO represents ; early departure  un-assured early arrival.
  41. 42. Non-FIFO Routing Concept
  42. 43. Key Questions... When is the best time to depart a particular node as you journey? Which is the optimal next hop node at that time? Which route is less prone to Incident risk ?
  43. 44. 3D View of Wait-time Set
  44. 45. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  45. 46. Chabini’s DOT (Decrease Order Of Time) Recursion Algorithm  denote the non-negative time required to travel from node j to node i  denote the total travel time associated with the current shortest path from node i to the destination node D at time t.  denote the sets of nodes directly connected to node D.
  46. 47. Stage Diagram; Wait time Search
  47. 48. Recursive Wait-time Search
  48. 49. Sequential Algorithm
  49. 50. WSDOT with Risk
  50. 51. Thesis Outline <ul><li>Problem Description </li></ul><ul><li>Thesis Contributions </li></ul><ul><li>ATIS Data </li></ul><ul><li>Arc Volume Estimation </li></ul><ul><li>Using Spatial Relationship </li></ul><ul><li>EM-BS Algorithm </li></ul><ul><li>Case Study - Col-Glen Road </li></ul><ul><li>Probability of Incident Risk </li></ul><ul><li>non-FIFO Routing concept </li></ul><ul><li>Hu and Chan Algorithm </li></ul><ul><li>WSDOT Algorithm </li></ul><ul><li>WSDOT_Risk Algorithm </li></ul><ul><li>Case Study – Central Arkansas </li></ul><ul><li>Mini TMC </li></ul><ul><li>Live Demo </li></ul><ul><li>Conclusion </li></ul>
  51. 52. Network Graph
  52. 53.
  53. 54.
  54. 55. CFVD
  55. 56. Online Service; A mini-TMC
  56. 57. ITS metaLab miniTMC Demo http:// syseng.ualr.edu/metalab/research /
  57. 59. Conclusion Presented are new concepts that would help power a functional ATIS. We present a non-FIFO type algorithm WSDOT-Risk which would help drivers get faster travel time and simultaneously avoid incident risks en-route to their destination. We developed mathematical models to compute Incident risk as a function of time (either peak & off-peak or continuous). We also discussed the importance of real-time traffic information for an ATIS, should in case there is partial information, traffic information can be estimated in the spatial dimension using upstream and downstream relationship of network arcs. An extension to this could be in the temporal dimension, in which we can forecast some time into the future based on neighboring arcs.
  58. 60. References [1] Dynamic Routing to Minimize Travel Time and Incident Risks (J.Hu and Y.Chan). [2] Chabini, I. A new shortest algorithm for discrete dynamic networks, Proceedings of the 8th IFAC Symposium on Transport System, China, Greece, Jun. 16-17, 1997, pp. 551-556 [3] Chabini, I. Discrete dynamic shortest path problems in transportation application: Complexity and algorithms with optimal run time, Transportation Research Record 1645, 1998, pp. 170-175. [4] Ziliaskopoulos, A. K. and Mahmassani, H. S. Design and implementation of a shortest path algorithm with time-dependent arc costs, Proceedings of 5 th advanced technology conference, Washington, D. C., 1992, pp. [5] Ziliaskopoulos, A. K. and Mahmassani, H. S. Time-dependent, shortest-path algorithm for real-time intelligent vehicle highway system applications, Transportation Research Record 1408, 1993, pp 94-100. [6] Chan, Y. Location Transport and Land-Use: Modeling Spatial-Temporal Information. Springer, Berlin – New York, 2005, pp. 506. [7] Farradyne, P. B. et al. Arkansas Statewide Intelligent Transportation Systems (ITS) Strategic Plan, Prepared for Arkansas State Highway & Transportation Department, 2002. [8] Metroplan. Intelligent Transportation System, Central Arkansas Regional Transportation Study, June, 2002. [9] Bellman, R. On a routing problem. Quart. Appl. Mathematics, Vol. 16, 1958, pp. 87-90.

×