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Appl. Sci. 2017, 7(6), 588; doi:10.3390/app7060588

Stochastic Model Predictive Control for Urban Traffic Networks

1
College of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China
2
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310024, China
3
College of Biological, Chemical Sciences and Engineering, Jiaxing University, Jiaxing 314001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 13 April 2017 / Revised: 19 May 2017 / Accepted: 2 June 2017 / Published: 7 June 2017
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Abstract

This paper proposes a stochastic model predictive control (MPC) framework for traffic signal coordination and control in urban traffic networks. One of the important features of the proposed stochastic MPC model is that uncertain traffic demands and stochastic disturbances are taken into account. Aiming to effectively model the uncertainties and avoid queue spillback in traffic networks, we develop a stochastic expected value model with chance constraints for the objective function of the stochastic MPC model. The objective function is defined to minimize the queue length and the oscillation of green time between any two control steps. Furthermore, by embedding the stochastic simulation and neural networks into a genetic algorithm, we propose a hybrid intelligent algorithm to solve the stochastic MPC model. Finally, numerical results by means of simulation on a road network are presented, which illustrate the performance of the proposed approach. View Full-Text
Keywords: urban traffic signal control; model predictive control; genetic algorithm; neural networks urban traffic signal control; model predictive control; genetic algorithm; neural networks
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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. (CC BY 4.0).

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MDPI and ACS Style

Ye, B.-L.; Wu, W.; Gao, H.; Lu, Y.; Cao, Q.; Zhu, L. Stochastic Model Predictive Control for Urban Traffic Networks. Appl. Sci. 2017, 7, 588.

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