Abstract
Over the past several decades, the development of technologies and the production of autonomous vehicles have enhanced the need for intelligent intersection management systems. Subsequently, growing interest in studying the traffic management of autonomous vehicles at intersections has been evident, which indicates a critical need to conduct a systematic literature review on this topic. This paper offers a systematic review of the proposed methodologies for intelligent intersection management systems and presents the remaining research gaps and possible future research approaches. We consider both pure autonomous vehicle traffic and mixed traffic at four-way signalized and unsignalized intersection(s). We searched for articles published from 2008 to 2019, and identified 105 primary studies. We applied the thematic analysis method to analyze the extracted data, which led to the identification of four main classes of methodologies, namely rule-based, optimization, hybrid, and machine learning methods. We also compared how well the methods satisfy their goals, namely efficiency, safety, ecology, and passenger comfort. This analysis allowed us to determine the primary challenges of the presented methodologies and propose new approaches in this area.
Highlights
The rapid population growth and the attendant increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, with traffic congestion forecast to increase by 60% by 2030 [1]
2) RESULTS OF RQ2 We divided this question into two sub-questions that yielded the following results: a: RESULTS OF RQ2.1 we focus on intelligent intersection management methodologies with pure autonomous vehicles (AVs) traffic
The results showed that the proposed method can improve evacuation time and reduce average queue length and average vehicle waiting time
Summary
The rapid population growth and the attendant increase in vehicle numbers over the last few decades have caused traffic congestion worldwide, with traffic congestion forecast to increase by 60% by 2030 [1]. Because intersections significantly impact the efficiency of traffic management systems in urban areas, this study focuses on intelligent traffic management systems at intersections. Research has shown that intersections play a critical role in collision numbers and traffic delays in urban areas [3]. Traffic delays, which affect congestion costs, The associate editor coordinating the review of this manuscript and approving it for publication was Zhengbing He. TO AV AND INTELLIGENT TRAFFIC MANAGEMENT we present a brief description of AVs, intelligent transportation systems, and autonomous intersection management. What factors did intelligent intersection management studies address in terms of utilizing AVs? What kinds of methodologies have been proposed to address the potential problems related to intelligent intersection management systems?
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