Integration of Signal Control and Transit Signal Priority Optimization in Coordinated Network Using Genetic Algorithms and Artificial Neural Networks

Many transit agencies are currently considering implementing priority systems providing buses with temporary green signal extensions and early green recalls at urban signalized intersections. While many studies have evaluated the potential for bus delay reductions and negative traffic impacts, most of these studies focused on implementations within traditional fixed-time traffic signal control systems. Only a few studies have addressed the problems of integrating transit signal priority (TSP) in coordinated real-time traffic signal control systems. A particular problem in this case is the uncertainty of predicting transit movements when considering the variability of dwell times at service stops. This study presents the development of a real-time traffic signal controller integrating traffic signal timing optimization and TSP control using a Genetic Algorithm (GA) and an Artificial Neural Networks (ANN) modeling. The GA is used to find near-optimal signal timings while the ANN is used to predict the travel of buses along transit routes. Evaluation results show that the proposed integrated controller can reduce transit delay, improve schedule adherence and service reliability, and benefit non-transit traffic compared to traditional fixed-time control with and without TSP, as well as a real-time GA-based control approach without TSP.

Language

  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 21p
  • Monograph Title: TRB 88th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01126612
  • Record Type: Publication
  • Report/Paper Numbers: 09-3063
  • Files: TRIS, TRB
  • Created Date: Apr 17 2009 9:56AM