Title page for ETD etd-05172000-13150029


Type of Document Dissertation
Author Zhang, Li
Author's Email Address li.zhang@vt.edu
URN etd-05172000-13150029
Title Optimizing Traffic Network Signals Around Railroad Crossings
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Hobeika, Antoine G. Committee Chair
Ghaman, Raj Committee Member
Lin, Wei-hua Committee Member
Trani, Antonio A. Committee Member
Woerner, Brian D. Committee Member
Keywords
  • Traffic Signal Optimization
  • Neural Network
  • Intelligent Agent
  • Grade Crossing Safety
Date of Defense 2000-05-12
Availability unrestricted
Abstract
The dissertation proposed an approach, named “Signal Optimization Under Rail Crossing sAfety cOnstraints”(SOURCAO), to the traffic signal control near a highway rail grade crossing (HRGC). SOURCAO targets two objectives: HRGC safety improvement (a high priority national transportation goal) and highway traffic delay reduction (a common desire for virtually all of us). Communication and data availability from ITS and the next generation train control are assumed available in SOURCAO.

The first step in SOURCAO is to intelligently choose a proper preemption phase sequence to promote HRGC safety. An inference engine is designed in place of traditional traffic signal preemption calls to prevent the queue from backing onto HRGC. The potential hazard is dynamically examined as to whether any queuing vehicle stalls on railroad tracks. The inference engine chooses the appropriate phase sequence to eliminate the hazardous situation.

The second step in SOURCAO is to find the optimized phase length. The optimization process uses the network traffic delay (close to the control delay) at the intersections within HRGC vicinities as an objective function. The delay function is approximated and represented by multilayer perceptron neural network (off-line). After the function was trained and obtained, an optimization algorithm named Successive Quadratic Programming (SQP) searches the length of phases (on-line) by minimizing the delay function. The inference engine and proposed delay model in optimization take the on-line surveillance detector data and HRGC closure information as input.

By integrating artificial intelligence and optimization technologies, the independent simulation evaluation of SOURCAO by TSIS/CORSIM demonstrated that the objectives are reached. The average network delay for 20 runs of simulation evaluation is reduced over eight percent by a t-test while the safety of HRGC is promoted. The sensitivity tests demonstrate that SOURCAO works efficiently under light and heavy traffic conditions, as well as a wide range of HRGC closure times.

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