Next Article in Journal
Computational Vibroacoustics in Low- and Medium- Frequency Bands: Damping, ROM, and UQ Modeling
Previous Article in Journal
Photon Propagation through Linearly Active Dimers
Article Menu
Issue 6 (June) cover image

Export Article

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

Stochastic Model Predictive Control for Urban Traffic Networks

College of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310024, China
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
View Full-Text   |   Download PDF [895 KB, uploaded 8 June 2017]   |  


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

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top