State-of-art review of traffic signal control methods: challenges and opportunities
TLDR
A critical review of some of the widely used microsimulation packages is provided in this paper, intended to provide insights into the future of research in these areas.Abstract:
Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.read more
Citations
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A memetic algorithm for real world multi-intersection traffic signal optimisation problems
TL;DR: In this article, an adaptive memetic algorithm (MA) was proposed to optimize signal timings in real world urban road networks using traffic volumes derived from induction loop detectors, in an adaptive manner, so as to accelerate the search process and generate high quality solutions.
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Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach
TL;DR: A novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed, showing that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method.
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Intersection Signal Timing Optimization: A Multi-Objective Evolutionary Algorithm
TL;DR: The method of combining hybrid constraint strategy and NSGA-III framework is introduced and the results show that the indices of traffic capacity, delay and exhaust emission obtained by the proposed method have more obvious advantages.
Proceedings ArticleDOI
Smart Mobility Implementation in Smart Cities: A Comprehensive Review on State-of-art Technologies
TL;DR: The most common components of a smart city as discussed in the existing literature such as smart economy, smart governance, smart living, smart people, smart environment, and smart mobility are highlighted and a comprehensive review is conducted on state-of-art technology involved in the implementation of smart mobility.
Proceedings ArticleDOI
Traffic Signal Control: a Double Q-learning Approach
TL;DR: In this paper, the authors investigated the reinforcement learning approach, namely, the double Q-learning approach, to solve the traffic signal control problem in a smart city environment, where both the initial data on the connected vehicles distribution and the aggregated characteristics of traffic flows are used to describe the state of the RL agent, and the proposed model were carried out on synthetic and real data using the CityFlow microscopic traffic simulator.
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