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As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, 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. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection’s stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues.

Many traffic models and control methods have already been utilized in the public transportation system due to the increasing traffic congestion. Thus, an intelligent traffic model is formalized and presented to control multiple traffic light simultaneously and efficiently according to the distribution of vehicles from each incoming link (i.e. sections) in this paper. Compared with constant strategy, two methods are proposed for traffic light control, i.e., game theoretical strategy and reinforcement learning methods. Game theoretical strategy is generated in a game theoretical framework where incoming links are regarded as players and the combination of the status of traffic lights can be regarded as decisions made by these players. The cost function is evaluated and the strategy is produced with Nash equilibrium for passing maximum vehicles in an intersection. The other one is Single-Agent Reinforcement Learning (SARL), specifically with the Q-learning algorithm in this case, which is usually used in such a dynamic environment to control traffic flow so the traffic problem could be improved. Generally, the intersection is regarded as the centralized agent and controlling signal status is considered as the actions of the agent. The performance of these two methods is compared after simulated and implemented in a junction.

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

Deep reinforcement learning is a data-driven method, which is very promising for alleviating traffic congestion through intelligent control of traffic lights. In this paper, the traffic signal of an intersection is divided into four independent phases and then controlled by deep Q-network (DQN) models respectively. Models can receive observations from their own angle of view, i.e., north-south straight, north-south left turn, east-west straight, east-west left turn, instead of extracting features from the whole scene. We suppose that it is beneficial for learning better policy if agents could sense the environment more precisely. DQN models are jointly trained under the revised QMIX framework to promote coordination capability. For decentralized execution, traffic lights of the phase with the highest Q-value will turn green. The experiments are done under SUMO, the results demonstrate that our method obtains higher reward and lower delay compared to controlling the holistic cycle by using a single DQN model.

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches.

Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications.

Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light timing optimization method based on double dueling deep Q-network, MaxPressure, and Self-organizing traffic lights (SOTL), namely EP-D3QN, which controls traffic flows by dynamically adjusting the duration of traffic lights in a cycle, whether the phase is switched based on the rules we set in advance and the pressure of the lane. In EP-D3QN, each intersection corresponds to an agent, and the road entering the intersection is divided into grids, each grid stores the speed and position of a car, thus forming the vehicle information matrix, and as the state of the agent. The action of the agent is a set of traffic light phase in a signal cycle, which has four values. The effective duration of the traffic lights is 0–60 s, and the traffic light phases switching depends on its press and the rules we set. The reward of the agent is the difference between the sum of the accumulated waiting time of all vehicles in two consecutive signal cycles. The SUMO is used to simulate two traffic scenarios. We selected two types of evaluation indicators and compared four methods to verify the effectiveness of EP-D3QN. The experimental results show that EP-D3QN has superior performance in light and heavy traffic flow scenarios, which can reduce the waiting time and travel time of vehicles, and improve the traffic efficiency of an intersection.

Traffic congestion in urban areas poses several challenges to municipal authorities including pollution, productivity loss, reckless driving, and delays in dealing with emergencies. Smart cities can use modern IoT infrastructure to solve the congestion problem and reduce pollution and delays. In this article, we focus on congestion mapping and mitigation for emergency vehicles in smart cities. We use a novel traffic light control technique to change the flow of cars on lights of interest thereby making way for emergency vehicles. We use a simulation model for a selected area of Manhattan to implement congestion mapping and to help find the fastest path for routing emergency vehicles based on the congestion metrics. The system controls traffic lights to block off the roads feeding into congestion and allows flow away from the congested path. This helps in clearing the preferred route to help emergency vehicles reach the destination faster. We show that the proposed algorithm can map congestion on city roads with accuracy thus helping to improve the response time of the emergency services and saving precious lives.

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport.