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Bi‐level Programming Formulation and Heuristic Solution Approach for Dynamic Traffic Signal Optimization
Author(s) -
Sun Dazhi,
Benekohal Rahim F.,
Waller S. Travis
Publication year - 2006
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2006.00439.x
Subject(s) - heuristic , computer science , mathematical optimization , cell transmission model , genetic algorithm , signal (programming language) , dynamic programming , traffic flow (computer networking) , population , flow network , optimization problem , real time computing , algorithm , engineering , traffic congestion , mathematics , computer network , demography , sociology , transport engineering , programming language
Although dynamic traffic control and traffic assignment are intimately connected in the framework of Intelligent Transportation Systems (ITS), they have been developed independent of one another by most existing research. Conventional methods of signal timing optimization assume given traffic flow pattern, whereas traffic assignment is performed with the assumption of fixed signal timing. This study develops a bi‐level programming formulation and heuristic solution approach (HSA) for dynamic traffic signal optimization in networks with time‐dependent demand and stochastic route choice. In the bi‐level programming model, the upper level problem represents the decision‐making behavior (signal control) of the system manager, while the user travel behavior is represented at the lower level. The HSA consists of a Genetic Algorithm (GA) and a Cell Transmission Simulation (CTS) based Incremental Logit Assignment (ILA) procedure. GA is used to seek the upper level signal control variables. ILA is developed to find user optimal flow pattern at the lower level, and CTS is implemented to propagate traffic and collect real‐time traffic information. The performance of the HSA is investigated in numerical applications in a sample network. These applications compare the efficiency and quality of the global optima achieved by Elitist GA and Micro GA. Furthermore, the impact of different frequencies of updating information and different population sizes of GA on system performance is analyzed.