Turning-lane and signal optimization at intersections with multiple objectives

ABSTRACT

Traffic congestion at intersections is a serious problem in cities. In order to discharge turning vehicles efficiently at intersections to relieve traffic jams, multiple left-turn and right-turn lanes are often used. This article proposes a novel multi-objective optimization method for signal setting and multiple turning-lane assignment at intersections based on microscopic traffic simulations and a cell-mapping method. Vehicle conflicts and pedestrian interference are considered. The intersection multi-objective optimization problem (MOP) is formulated. The cell-mapping method is adopted to solve the MOP. Three measures of traffic performance are studied including transportation efficiency, energy consumption and road safety. The influence of turning-lane assignment on intersection performance is investigated in the optimization. Significant impacts of the number of turning lanes on the traffic are observed. An algorithm is proposed to assist traffic engineers to select and implement the optimal designs. In general, more turning lanes help increase turning traffic efficiency and lower fuel consumption in most cases. Remarkable improvement in traffic performance can be achieved with combined optimization of lane assignment and signal setting, which cannot be obtained with signal setting optimization alone. The studies reported in this article provide general guidance for intersection planning and operation. The proposed optimization methodology represents a promising emerging technology for traffic applications.

KEYWORDS:

• Multi-objective optimization
• turning-lane assignment
• signal setting
• traffic simulation
• intersection

Acknowledgments

Xiang Li would like to thank the Department of Mechanics, Tianjin University, for support, and the China Scholarship Council (CSC) for sponsoring his study in the United States of America.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The material in this article is based on work supported by the Fundamental Research Funds for the Central Universities [grant numbers N170503012 and N170308028]; the National Natural Science Foundation of China [grant numbers 11172197, 11332008 and 11572215]; and a grant from the University of California Institute for Mexico and the United States (UC MEXUS) and the Consejo Nacional de Ciencia y Tecnología de México (CONACYT) through the project ‘Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-Objective Optimization Algorithms’.