Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Arshad Jamal, Muhammad Tauhidur Rahman, Hassan M. Al-Ahmadi, Irfan Ullah and Muhammad Zahid
Additional contact information
Arshad Jamal: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5055, Dhahran 31261, Saudi Arabia
Muhammad Tauhidur Rahman: Department of City and Regional Planning, King Fahd University of Petroleum & Minerals, KFUPM Box 5053, Dhahran 31261, Saudi Arabia
Hassan M. Al-Ahmadi: Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, KFUPM Box 5055, Dhahran 31261, Saudi Arabia
Irfan Ullah: School of Transportation and Logistics, Dalian University of Technology, Dalian 116024, China
Muhammad Zahid: College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China

Sustainability, 2020, vol. 12, issue 5, 1-23

Abstract: Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

Keywords: signalized intersections; delay optimization; meta-heuristic; genetic algorithm; differential evolution; TRANSYT 7F; Dhahran (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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