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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 803647, 13 pages
Research Article

A Simulation-Based Framework for the Cooperation of VMS Travel Guidance and Traffic Signal Control

1Department of Civil Engineering, Tsinghua University, 315 Shanheng He Building, Beijing 100084, China
2Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China

Received 14 January 2014; Revised 19 May 2014; Accepted 19 May 2014; Published 16 June 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Meng Li 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.


Nowadays, both travel guidance systems and traffic signal control systems are quite common for urban traffic management. In order to achieve collaborative effect, different models had been proposed in the last two decades. In recent years, with the development of variable message sign (VMS) technology, more and more VMS panels are installed on major arterials to provide highly visible and concise graphs or text messages to drivers, especially in developing countries. To discover drivers’ responses to VMS, we establish a drivers’ en route diversion model according to a stated-preference survey. Basically, we proposed a cooperative mechanism and systematic framework of VMS travel guidance and major arterials signal operations. And then a two-stage nested optimization problem is formulated. To solve this optimization problem, a simulation-based optimization method is adopted to optimize the cooperative strategies with TRANSIMS. The proposed method is applied to the real network of Tianjin City comprising of 30 nodes and 46 links. Simulations show that this new method could well improve the network condition by 26.3%. And analysis reveals that GA with nested dynamic programming is an effective technique to solve the optimization problem.