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Mathematical Problems in Engineering
Volume 2018 (2018), Article ID 2453246, 11 pages
https://doi.org/10.1155/2018/2453246
Research Article

A General Maximum Progression Model to Concurrently Synchronize Left-Turn and through Traffic Flows on an Arterial

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510640, China

Correspondence should be addressed to Binbin Jing; moc.621@91nibgnij

Received 10 October 2017; Revised 8 January 2018; Accepted 21 January 2018; Published 21 February 2018

Academic Editor: Luca D’Acierno

Copyright © 2018 Binbin Jing and Jianmin Xu. 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.

Abstract

In the existing bandwidth-based methods, through traffic flows are considered as the coordination objects and offered progression bands accordingly. However, at certain times or nodes in the road network, when the left-turn traffic flows have a higher priority than the through traffic flows, it would be inappropriate to still provide the progression bands to the through traffic flows; the left-turn traffic flows should instead be considered as the coordination objects to potentially achieve better control. Considering this, a general maximum progression model to concurrently synchronize left-turn and through traffic flows is established by using a time-space diagram. The general model can deal with all the patterns of the left-turn phases by introducing two new binary variables into the constraints; that is, these variables allow all the patterns of the left-turn phases to deal with a single formulation. By using the measures of effectiveness (average delay time, average vehicle stops, and average travel time) acquired by a traffic simulation software, VISSIM, the validity of the general model is verified. The results show that, compared with the MULTIBAND, the proposed general model can effectively reduce the delay time, vehicle stops, and travel time and, thus, achieve better traffic control.