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Mathematical Problems in Engineering
Volume 2014, Article ID 430497, 10 pages
Research Article

Real-Time Arterial Coordination Control Based on Dynamic Intersection Turning Fractions Estimation Using Genetic Algorithm

School of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Received 4 June 2014; Accepted 3 July 2014; Published 16 July 2014

Academic Editor: Bin Yu

Copyright © 2014 Pengpeng Jiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Real-time arterial coordination control is crucial for urban transportation systems and is partially dependent on dynamic turning flows at intersections. Few existing researches employ such information due to the restrictions of traffic surveillance systems. This paper presents a model framework for real-time arterial coordination control based on dynamic intersection turning fraction estimation, including three submodels: (1) a parameter optimization model to estimate dynamic intersection turning fractions using detected link counts at entering and exiting approaches; (2) a nonlinear model using minimum delay as an objective to optimize the time-varying public cycle for the arterial road based on the estimated turning flows; and (3) a revised optimization model to achieve real-time offset and split for the arterial road using the novel uninterrupted ratio as objective function. Two revised genetic algorithms are developed to solve the first and third submodels, respectively, and an ordinary optimization algorithm is designed for the second submodel. Time-varying public cycle, offset, and split constitute the real-time arterial coordination control scheme together. The general model framework removes most of the assumptions of conventional arterial control models and provides a time-varying timing plan. Simulation experiments using actual data indicate that the proposed model yields much better results than the existing methods.