Journal
SMART CITIES
Volume 5, Issue 4, Pages 1293-1311Publisher
MDPI
DOI: 10.3390/smartcities5040066
Keywords
adaptive traffic signal control; intelligent transportation systems; internet of things; artificial intelligence; machine learning; multi-agent reinforcement learning
Ask authors/readers for more resources
This paper focuses on the real-world scenario of Shiraz City and proposes a more efficient method for controlling traffic lights using IoT and AI technologies. By utilizing distributed Multi-Agent Reinforcement Learning and real-time traffic data, an intelligent traffic signal control system is provided to improve overall traffic congestion. Simulation results show that this approach outperforms traditional fixed-time traffic signal control scheduling in terms of average vehicle queue lengths and waiting times at intersections.
Traffic is one of the indispensable problems of modern societies, which leads to undesirable consequences such as time wasting and greater possibility of accidents. Adaptive Traffic Signal Control (ATSC), as a key part of Intelligent Transportation Systems (ITS), plays a key role in reducing traffic congestion by real-time adaptation to dynamic traffic conditions. Moreover, these systems are integrated with Internet of Things (IoT) devices. IoT can lead to easy implementation of traffic management systems. Recently, the combination of Artificial Intelligence (AI) and the IoT has attracted the attention of many researchers and can process large amounts of data that are suitable for solving complex real-world problems about traffic control. In this paper, we worked on the real-world scenario of Shiraz City, which currently does not use any intelligent method and works based on fixed-time traffic signal scheduling. We applied IoT approaches and AI techniques to control traffic lights more efficiently, which is an essential part of the ITS. Specifically, sensors such as surveillance cameras were used to capture real-time traffic information for the intelligent traffic signal control system. In fact, an intelligent traffic signal control system is provided by utilizing distributed Multi-Agent Reinforcement Learning (MARL) and applying the traffic data of adjacent intersections along with local information. By using MARL, our goal was to improve the overall traffic of six signalized junctions of Shiraz City in Iran. We conducted numerical simulations for two synthetic intersections by simulated data and for a real-world map of Shiraz City with real-world traffic data received from the transportation and municipality traffic organization and compared it with the traditional system running in Shiraz. The simulation results show that our proposed approach performs more efficiently than the fixed-time traffic signal control scheduling implemented in Shiraz in terms of average vehicle queue lengths and waiting times at intersections.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available