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.
Due to enhance of number of motor vehicles, the amount of consuming of the world's oil supply is increased and it is the major source of pollution to the surroundings. Base on this, it is vital to use different techniques to provide more opportunities to identify strategies that can help to utilize more efficient from the current transport resources. In Yemen, the rapid increase in the use of the private vehicles has resulted in increased traffic congestion, accidents, inadequate parking space and air pollution, among other problems. Base on these problems, this study attempts to focus on some factors which lead to rise in private vehicle ownership, motorcycle related concern, demand of more public transport, traffic congestion, parking, road safety and air pollution. Hence, this study tries to rectify the major concerns of traffic congestion and other environment hazards. When the mentioned elements are addressed efficiently, the effective contribution can make successful urban development.
Due to increase of the urbanization and raising the number of the vehicles road traffic incident and delay time in traffic jam are the main concern of countries in all over the world. Due to this problem drivers face an elevated crash risk especially when drivers on freeway ramp interchanges compared with other sections of freeways. The definition of ramp was based on the type and number of lanes used by traffic to freeways. The vehicle accidents are prevalent on highway ramps because of over-speeding, related to the characteristics and circumstances of ramps. Site survey and observation has been done for each location to identify the road condition, adjacent environment and vehicle operations. SPSS is used to analysis the accidents that they are collected data from case study. Descriptive analyses are performed by output of the analyzing data of accident. Differences in accident rates are due to driver behavior, weather potential safety issues were identified at interchanges. Ramps are scheduled for auditing based on descending road speed limits.
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