Connected Vehicle Based Traffic Signal Optimization

Connected vehicles (CVs) in smart cities, including vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to anything (V2X) communications, can provide more opportunities and impose more challenges for urban traffic signal control. This project aims to develop a framework, including modeling techniques, algorithms, and testing strategies, for urban traffic signal optimization with CVs. This framework is able to optimize traffic signal timing for a single intersection or along a corridor. More specifically, the major tasks of this project include: (1) Development of CV-based traffic signal timing optimization methods utilizing individual vehicles’ trajectories (i.e., second-by-second vehicle locations and speeds). This includes methods for timing plan optimization (of a single intersection) and coordination optimization among multiple intersections. The proposed method evaluates the total weighted sum of travel times and fuel consumption of all vehicles in the study area in the optimal green time and offset determination. (2) Propose solution methods for CV-based traffic signal optimization, which includes dynamic programming (DP) with two-step method for intersection level optimization (phase duration optimization) and a prediction-based solution method for the two-level problem (offset optimization) under corridor level optimization. (3) Comprehensive testing and validation of the proposed methods in traffic simulation. Various combinations of travel demands and types of CVs are tested for the proposed signal timing optimization methods. The testing tasks should validate that the developed methods are computationally manageable and have the potential to be implemented in CV-based traffic signal applications in the real world.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 54p

Subject/Index Terms

Filing Info

  • Accession Number: 01677559
  • Record Type: Publication
  • Created Date: Jul 27 2018 8:15AM