Fowe Thesis Full

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.

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.

Thesis Outline

Problem Description

Thesis Contributions

ATIS Data

Arc Volume Estimation

Using Spatial Relationship

EM-BS Algorithm

Case Study - Col-Glen Road

Probability of Incident Risk

non-FIFO Routing concept

Hu and Chan Algorithm

WSDOT Algorithm

WSDOT_Risk Algorithm

Case Study – Central Arkansas

Mini TMC

Live Demo

Conclusion

ATIS ( Advanced Traveler Information System)

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

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)

A mathematical model to compute Time-dependent Incident Probabilities from historical traffic and incident data. (Fall 08)

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)

A mathematical algorithm to estimate traffic volumes in unknown arcs using partial information from upstream/downstream neighbors and entropy maximization. (Spring 09)

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)

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Data Collection Systems

Inductive Loop Sensors

Traffic/Surveillance Cameras

Aerial Photos

CFVD (Cellular Floating Vehicle Data)

Video Vehicle Detection Systems

Data is Key to a Functional ATIS

Partial Information (incomplete Data)

Perfect Information (Complete Data)

Static/Persistent Data (Average & non-time dependent).

Dynamic Data (Time Dependent)

Real-Time Data (Streaming and Complete Time Dependent Data)

Have Need

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Forecasting

Spatial Relationships In both spatial and temporal Dimensions; Closer by neighbors have tendency to be similar than farther away neighbors.

Spatial Matrix First –Order Spatial Arc Neighbors

Spatial Matrix Second –Order Spatial Arc Neighbors

Spatial Weights

Model Formulation

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Arc Volume Estimate Naïve Solution

Entropy Maximization - Discrete

Entropy Maximization with Binary Search (EMBS)

Complexity Analysis

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

Sample Computation

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Time Dependent Incident Probability

Fundamental Diagram of Traffic Flow

Time Dependent Incident Probability – Peak vs Off-Peak

Poisson – Time Dependent Probability Model

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

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.

Non-FIFO Routing Concept

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 ?

3D View of Wait-time Set

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

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.

Stage Diagram; Wait time Search

Recursive Wait-time Search

Sequential Algorithm

WSDOT with Risk

Thesis Outline

Problem Description

ATIS Data

EM-BS Algorithm

WSDOT Algorithm

Mini TMC

Live Demo

Conclusion

Network Graph

CFVD

Online Service; A mini-TMC

ITS metaLab miniTMC Demo http:// syseng.ualr.edu/metalab/research /

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.

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.