Title page for ETD etd-04242004-151851

Type of Document Master's Thesis
Author Jagannathan, Ramanujan
Author's Email Address rjaganna@vt.edu
URN etd-04242004-151851
Title Evaluation of Crossover Displaced Left-turn (XDL) Intersections and Real-time Signal Control Strategies with Artificial Intelligence Techniques
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Teodorovic, Dusan Committee Chair
Collura, John Committee Member
Tignor, Samuel C. Committee Member
  • Genetic Algorithms
  • CFI
  • XDL
  • Neural Networks
  • Continuous Flow Intersections
  • Real-time Signal Control
Date of Defense 2003-12-08
Availability unrestricted
Although concepts of the XDL intersection or CFI (Continuous Flow Intersection) have been around for approximately four decades, users do not yet have a simplified procedure to evaluate its traffic performance and compare it with a conventional intersection. Several studies have shown qualitative and quantitative benefits of the XDL intersection without providing accessible tools for traffic engineers and planners to estimate average control delays, and queues. Modeling was conducted on typical geometries over a wide distribution of traffic flow conditions for three different design configurations or cases using VISSIM simulations with pre-timed signal settings. Some comparisons with similar conventional designs show considerable savings in average control delay, and average queue length and increase in intersection capacity. The statistical models provide an accessible tool for a practitioner to assess average delay and average queue length for three types of XDL intersections. Pre-timed signal controller settings are provided for each of the five intersections of the XDL network.

In this research, a “real-time” traffic signal control strategy is developed using genetic algorithms and neural networks to provide near-optimal traffic performance for XDL intersections. Knowing the traffic arrival pattern at an intersection in advance, it is possible to come up with the best signal control strategy for the respective scenario. Hypothetical cases of traffic arrival patterns are generated and genetic algorithms are used to come up with near-optimal signal control strategy for the respective cases. The neural network controller is then trained and tested using pairs of hypothetical traffic scenarios and corresponding signal control strategies. The developed neural network controller produces near-optimal traffic signal control strategy in “real-time” for all varieties of traffic arrival patterns.

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