DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS

Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic, decreasing traffic pressure and lowering average travel time for drivers. In this paper we consider the problem of traffic signal control, present the basics of reinforcement learning and review the latest results in this area.

Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

Control and Coordination of Self-Adaptive Traffic Signal Using Deep Reinforcement Learning

The most observable obstacle to sustainable mobility is traffic congestions. These congestions cannot effectively be fixed by traditional control of traffic signals. Safe and smooth movement of traffic is ensured by a self-controlled traffic signal. As such, to coordinate the traffic flow it is necessary to implement dynamic traffic signal subsequences. Primarily, Traffic Signal Controllers (TSC) provides sophisticated control and coordination of vehicles. The control and coordination of traffic signal control systems can be effectively achieved by implementing the Deep Reinforcement Learning (DRL) approaches. The decision-making capabilities at intersections are improved by having variations of traffic signal timing using an adaptive TSC. Alternatively, the actual traffic demand is nothing but managing the traffic systems. It analyses the incoming number and type of vehicles and gives a real-time response at intersection geometrics and controls the traffic signals accordingly. The proposed DRL algorithm observes traffic data and operates optimum management plans for the regulation of the traffic flow. Furthermore, an existing traffic simulator is used to help provide a realistic environment to support the proposed algorithm.

A Survey on Deep Reinforcement Learning Network for Traffic Light Cycle Control

A Traffic signal control is a challenging problem and to minimize the travel time of vehicles by coordinating their movements at the road intersections. In recent years traffic signal control systems have on over simplified information and rule-based methods and we have large amounts of data, more computing power and advanced methods to drive the development of intelligent transportation. An intelligent transport system to use the machine learning methods likes reinforcement learning and to explain the acknowledged transportation approaches and a list of recent literature in traffic signal control. In this survey can foster interdisciplinary research on this important topic.

Accident Detection Traffic Light System with Dynamic Fuzzy Logic Control Using FuzzyTech Program and iTraffic Simulation

The development of traffic signal control systems has become one of the most important topics in this era. Traffic light controllers need to be improved continuously to solve the traffic problems. This paper discussed the proposed hybrid system and demonstrated how the system works from the beginning of the first flag “decrease of cross ratio” until the end of the action system. The proposed system was divided into three main parts: The proposed algorithm (Dynamic Webster with dynamic Cycle Time), Accident Detection System using fuzzy logic theory and Action System depending on Detection System. The focus of this paper is to discuss the accident detection system of the proposed hybrid system, which depended on fuzzy logic and its components. This paper also presented the results of FuzzyTech Software with different scenarios plotting the inputs outputs and the showcases the 3D plot for each one of them for detecting the accident. In addition, it presented results to measure the False Alarm Rate the Accident Detection Rate using FuzzyTech program.