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Mathematical Problems in Engineering
Volume 2016, Article ID 4015271, 11 pages
Research Article

Modeling Left-Turn Driving Behavior at Signalized Intersections with Mixed Traffic Conditions

1School of Mechanical Engineering, University of Science and Technology, Beijing 100083, China
2Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
3Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
4MKT & Solution Department, ZTE Corporation, Shenzhen 518057, China
5School of Mechanical and Electrical Engineering, North China University of Technology, Beijing 100144, China

Received 7 January 2016; Revised 13 April 2016; Accepted 18 April 2016

Academic Editor: Luca D’Acierno

Copyright © 2016 Hong 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.


In many developing countries, mixed traffic is the most common type of urban transportation; traffic of this type faces many major problems in traffic engineering, such as conflicts, inefficiency, and security issues. This paper focuses on the traffic engineering concerns on the driving behavior of left-turning vehicles caused by different degrees of pedestrian violations. The traffic characteristics of left-turning vehicles and pedestrians in the affected region at a signalized intersection were analyzed and a cellular-automata-based “following-conflict” driving behavior model that mainly addresses four basic behavior modes was proposed to study the conflict and behavior mechanisms of left-turning vehicles by mathematic methodologies. Four basic driving behavior modes were reproduced in computer simulations, and a logit model of the behavior mode choice was also developed to analyze the relative share of each behavior mode. Finally, the microscopic characteristics of driving behaviors and the macroscopic parameters of traffic flow in the affected region were all determined. These data are important reference for geometry and capacity design for signalized intersections. The simulation results show that the proposed models are valid and can be used to represent the behavior of left-turning vehicles in the case of conflicts with illegally crossing pedestrians. These results will have potential applications on improving traffic safety and traffic capacity at signalized intersections with mixed traffic conditions.