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Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic. [ABSTRACT FROM AUTHOR]

In urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non‐priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non‐priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non‐priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non‐priority traffic. [ABSTRACT FROM AUTHOR]

Compared to traditional traffic signal control methods, the method driven by Deep Reinforcement Learning (DRL) has shown better performance. But the problem of low sample utilization in reinforcement learning also arises. To deal with the problem, this paper presents a novel Twin Delayed Deep Deterministic Policy Gradient with Dual Buffer (TD3_DB) for traffic signal control. In the proposed framework, two experience buffers are used to store important samples and normal samples, separately, and the proportion of the two buffers is adjusted adaptively. In addition, lane pressure, describing the dynamic feature of lane traffic flow, is used for the state design of the TD3 agent, which enhances the perception of the agent toward intersections. Comprehensive experiments on different traffic flow modes has shown, the dual experience replay scheme can improve the sample utilization, and the proposed TD3_DB performs better than other methods such as original TD3, Proximal Policy Optimization (PPO), etc., effectively reducing vehicle queue length and waiting time. [ABSTRACT FROM AUTHOR]

Centralized traffic signal control has long been a challenging, high‐dimensional optimization problem. This study establishes a simulation‐based optimization framework and develops a novel optimization algorithm based on trust region Bayesian optimization (TuRBO), which can efficiently obtain an approximate optimal solution to the high‐dimensional traffic signal control problem. Local Gaussian process (GP), trust region, and Thompson sampling are employed in the TuRBO and contribute considerably to performance in terms of computational speed, solution quality, and scalability. Empirical studies are carried out using data from Mudanjiang and Chengdu, China. The performance of TuRBO is compared with that of Bayesian optimization (BO), genetic algorithm and random sampling. The results show that TuRBO converges the fastest because of its ability to balance exploration and exploitation through the trust region and Thompson sampling. Meanwhile, because TuRBO enables more efficient exploitation through the local GP, the solution quality of TuRBO outperforms others significantly. The average waiting time achieved by TuRBO was 2.84% lower than that achieved by BO. Finally, the method has been successfully extended to a large network with 233‐dimensional spaces and 122 signalized intersections, demonstrating that the developed methodology can deal with high‐dimensional traffic signal control effectively for real case applications. [ABSTRACT FROM AUTHOR]

Current literature on joint optimization of intersection signal timing and connected automated vehicle (CAV) trajectory mostly focuses on vehicular movements paying no or little attention to pedestrians. This paper presents a methodology to safely incorporate pedestrians into signalized intersections with CAVs and connected human‐driven vehicles (CHVs). The movements of vehicles are controlled using both traffic lights and mobile CAV controllers during our newly introduced "white phase." CAVs navigate platoons of CHVs through the intersection when the white phases are active. In addition to optimizing CAV trajectories, the model optimally selects the status of the traffic light signal among white and green indications for vehicular and walk and do‐not‐walk intervals for pedestrian movements. A receding horizon‐based methodology is used to capture the stochastic nature of the problem and to reduce computational complexity. The case study results show the successful operation of fleets consisting of pedestrians, CAVs, and CHVs with various demand levels through isolated intersections. The results also show that increasing the CAV market penetration rate (MPR) can decrease average intersection delay by up to 27%. Moreover, the average pedestrian, CHV, and CAV delays decrease as the CAV MPR increases and reach their minimum values with a fully CAV fleet. In addition, the presence of the white phase can decrease the intersection average delay by up to 14.7%. [ABSTRACT FROM AUTHOR]

To improve the effectiveness of model predictive control (MPC) in dynamic traffic signal control strategies, it has been combined with graph convolutional networks (GCNs) and deep reinforcement learning (DRL) technologies. In this study, a neural-network-based traffic signal control optimization method under the MPC framework is proposed. A dynamic correlation matrix is introduced in the predictive model to adapt to the dynamic changes in correlations between nodes over time. The signal control optimization strategy is solved using DRL, where the agent explores the optimal control strategy based on pre-set constraints in the future road environment. The geometric structure and traffic flow data of a real intersection were selected as the simulation validation environment, and a joint simulation was conducted using Python and SUMO. The experimental results indicate that in low-traffic scenarios, the queue length is reduced by more than 2 vehicles compared to the selected comparison methods; in high-traffic scenarios, the queue length is reduced by an average of 17 vehicles. Under the actual traffic data of the intersection, the average speed is increased by 6.4% compared to the fixed timing method; compared to the inductive signal control method, it increases from 9.76 m/s to 11.69 m/s, an improvement of 19.7%, effectively enhancing the intersection signal control performance. [ABSTRACT FROM AUTHOR]

One of the focal points in the field of intelligent transportation is the intelligent control of traffic signals (TS), aimed at enhancing the efficiency of urban road networks through specific algorithms. Deep Reinforcement Learning (DRL) algorithms have become mainstream, yet they suffer from inefficient training sample selection, leading to slow convergence. Additionally, enhancing model robustness is crucial for adapting to diverse traffic conditions. Hence, this paper proposes an enhanced method for traffic signal control (TSC) based on DRL. This approach utilizes dueling network and double q-learning to alleviate the overestimation issue of DRL. Additionally, it introduces a priority sampling mechanism to enhance the utilization efficiency of samples in memory. Moreover, noise parameters are integrated into the neural network model during training to bolster its robustness. By representing high-dimensional real-time traffic information as matrices, and employing a phase-cycled action space to guide the decision-making of intelligent agents. Additionally, utilizing a reward function that closely mirrors real-world scenarios to guide model training. Experimental results demonstrate faster convergence and optimal performance in metrics such as queue length and waiting time. Testing experiments further validate the method's robustness across different traffic flow scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing. [ABSTRACT FROM AUTHOR]

In this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow. [ABSTRACT FROM AUTHOR]

This paper introduces an approach to enhance the efficiency of urban intersections by integrating Visible Light Communication (VLC) into a multi-intersection traffic control system. The main objectives include the reduction in waiting times for vehicles and pedestrians, the improvement of overall traffic safety, and the accommodation of diverse traffic movements during multiple signal phases. The proposed system utilizes VLC to facilitate communication among interconnected vehicles and infrastructure. This is achieved by utilizing streetlights, headlamps, and traffic signals for transmitting information. By integrating VLC localization services with learning-driven traffic signal control, the multi-intersection traffic management system is established. A reinforcement learning scheme, based on VLC queuing/request/response behaviors, is utilized to schedule traffic signals effectively. Agents placed at each intersection control traffic lights by incorporating information from VLC-ready cars, including their positions, destinations, and intended routes. The agents devise optimal strategies to improve traffic flow and engage in communication to optimize the collective traffic performance. An assessment of the multi-intersection scenario through the SUMO urban mobility simulator reveals considerable benefits. The system successfully reduces both waiting and travel times. The reinforcement learning approach effectively schedules traffic signals, and the results highlight the decentralized and scalable nature of the proposed method, especially in multi-intersection scenarios. The discussion emphasizes the possibility of applying reinforcement learning in everyday traffic scenarios, showcasing the potential for the dynamic identification of control actions and improved traffic management. [ABSTRACT FROM AUTHOR]

In recent years, the increasing production and sales of automobiles have led to a notable rise in congestion on urban road traffic systems, particularly at ramps and intersections with traffic signals. Intelligent traffic signal control represents an effective means of addressing traffic congestion. Reinforcement learning methods have demonstrated considerable potential for addressing complex traffic signal control problems with multidimensional states and actions. In this research, the team propose Q-learning and Deep Q-Network (DQN) based signal control frameworks that use variable phase sequences and cycle times to adjust the order and the duration of signal phases to obtain a stable traffic signal control strategy. Experiments are simulated using the traffic simulator Simulation of Urban Mobility (SUMO) to test the average speed and the lane occupancy rate of vehicles entering the ramp to evaluate its safety performance and test the vehicle's traveling time to assess its stability. The simulation results show that both reinforcement learning algorithms are able to control cars in dynamic traffic environments with higher average speed and lower lane occupancy rate than the no-control method and that the DQN control model improves the average speed by about 10% and reduces the lane occupancy rate by about 30% compared to the Q-learning control model, providing a higher safety performance. [ABSTRACT FROM AUTHOR]

Efficient traffic signal management is crucial for regulating traffic flow and fostering sustainable development within road transportation systems. To address the challenges in traffic management, numerous studies have applied the Adaptive Traffic Signal Control (ATSC) technology, using Deep Reinforcement Learning (DRL) to decrease vehicles' average waiting times. Nonetheless, the intricate nature of DRL, characterized by its extensive parameter connections, often complicates the assurance of real-time responsiveness. Additionally, by prioritizing reduced waiting times, these methods may overlook potential rises in queue lengths, risking congestion. In this paper, we propose an Efficient Double Broad Reinforcement Learning (E-DBRL) algorithm based on a Double Broad Q-Network (Double BQN) to alleviate the overestimation of action values common in Broad Reinforcement Learning (BRL). To enhance the Quality of Experience (QoE) of drivers, we develop a new reward function that optimizes the average waiting time and the range between the longest and shortest waiting times, thus avoiding the need for dimension normalization. Moreover, we conduct simulation experiments using actual traffic data collected from Hangzhou, China. The experimental results indicate that, compared to the traditional Double DQN, the proposed E-DBRL algorithm achieves a 45.78% reduction in the average training time per round and a 5.57% increase in the average rewards. [ABSTRACT FROM AUTHOR]

Multi-Agent Reinforcement Learning (MARL) has shown strong advantages in urban multi-intersection traffic signal control, but it also suffers from the problems of non-smooth environment and inter-agent coordination. However, most of the existing research on MARL traffic signal control has focused on designing efficient communication to solve the environment non-smoothness problem, while neglecting the coordination between agents. In order to coordinate among agents, this paper combines MARL and the regional mixed-strategy Nash equilibrium to construct a Deep Convolutional Nash Policy Gradient Traffic Signal Control (DCNPG-TSC) model, which enables agents to perceive the traffic environment in a wider range and achieves effective agent communication and collaboration. Additionally, a Multi-Agent Distributional Nash Policy Gradient (MADNPG) algorithm is proposed in this model, which is the first time the mixed-strategy Nash equilibrium is used for the improvement in the Multi-Agent Deep Deterministic Policy Gradient algorithm traffic signal control strategy to provide the optimal signal phase for each intersection. In addition, the eco-mobility concept is integrated into MARL traffic signal control to reduce pollutant emissions at intersections. Finally, simulation results in synthetic and real-world traffic road networks show that DCNPG-TSC outperforms other state-of-the-art MARL traffic signal control methods in almost all performance metrics, because it can aggregate the information of neighboring agents and optimize the agent's decisions through gaming to find an optimal joint equilibrium strategy for the traffic road network. [ABSTRACT FROM AUTHOR]

Trajectory data from connected vehicles (CVs) provide a continuous and reliable means of obtaining information that can be leveraged to optimize traffic signals. This paper proposes a real‐time traffic signal control method using CV trajectory data as the sole input. The primary goal of the proposed signal control method is to prevent queue spillover, which may significantly decrease the traffic efficiency on urban networks and induce high delays to the travelers. The proposed method formulates the signal control problem via a linear quadratic optimization model, considering the constraints related to the duration and variability of green lights in practical traffic signal control systems. Compared to conventional max‐pressure‐based methods, the optimization model offers enhanced efficiency in handling these constraints, making it highly suitable for real‐life implementation. The proposed method has undergone testing in both simulated environments and real‐world applications. In the simulation experiments, the proposed method has been demonstrated to effectively reduce spillover risks and outperform a conventional max‐pressure‐based approach even when the CV penetration rate is as low as 5%. In the real‐world experiment, the proposed method had been tested in a traffic network around the Beijing Capital International Airport for several months. The severity of spillovers, which was represented by two performance indicators, had been significantly reduced after implementing the proposed method. [ABSTRACT FROM AUTHOR]

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system. [ABSTRACT FROM AUTHOR]

The integrated design of traffic signal control (TSC) and reversible lane (RL) is an effective way to solve the problem of tidal congestion with uncertainty at isolated intersections, because of its advantage in making full use of temporal‐spatial transportation facilities. Considering the contradiction between the dynamic TSC scheme and the fixed RL scheme in one period, a two‐stage optimization method based on improved mean‐standard deviation (MSD) model for isolated intersections with historical and real‐time uncertain traffic flow is proposed. In the first stage, applying the same‐period historical data of multiple days, a robust optimal traffic signal control model with reversible lane based on MSD model (MSD‐RTR model) is put forward to obtain the fixed RL scheme and the compatible initial TSC scheme. A double‐layer nested genetic algorithm (DN‐GA) is designed to solve this model. In the second stage, applying real‐time period data and multi‐day same‐period historical data, a robust optimal dynamic traffic signal control model based on MSD model (MSD‐RDT model) is put forward to obtain the dynamic TSC scheme. Three modes which reflect the different weights of historical period and real‐time period in this MSD‐RDT model are presented to improve the model stability, and a multi‐mode genetic algorithm (MM‐GA) is designed. Finally, a case study is presented to demonstrate the efficiency and applicability of the proposed models and algorithms. [ABSTRACT FROM AUTHOR]

Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans. [ABSTRACT FROM AUTHOR]

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight's remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios. [ABSTRACT FROM AUTHOR]

Traffic light optimization is known to be a cost-effective method for reducing congestion and energy consumption in urban areas without changing physical road infrastructure. However, due to the high installation and maintenance costs of vehicle detectors, most intersections are controlled by fixed-time traffic signals that are not regularly optimized. To alleviate traffic congestion at intersections, we present a large-scale traffic signal re-timing system that uses a small percentage of vehicle trajectories as the only input without reliance on any detectors. We develop the probabilistic time-space diagram, which establishes the connection between a stochastic point-queue model and vehicle trajectories under the proposed Newellian coordinates. This model enables us to reconstruct the recurrent spatial-temporal traffic state by aggregating sufficient historical data. Optimization algorithms are then developed to update traffic signal parameters for intersections with optimality gaps. A real-world citywide test of the system was conducted in Birmingham, Michigan, and demonstrated that it decreased the delay and number of stops at signalized intersections by up to 20% and 30%, respectively. This system provides a scalable, sustainable, and efficient solution to traffic light optimization and can potentially be applied to every fixed-time signalized intersection in the world. Without relying on any infrastructure-based vehicle detectors, the authors present a scalable traffic signal re-timing system that uses a small percentage of connected vehicle trajectories as the only input. Real-world tests demonstrate that the system decreases both delays and number of stops. [ABSTRACT FROM AUTHOR]

With the increasing number of urban vehicles, as well as the current situation of non-intelligent traffic control systems, spatiotemporal non-uniform traffic resource occupation, and limited traffic planning and design, existing urban traffic planning methods cannot effectively solve problems such as frequent traffic congestion and uncontrollable commuting time for residents. In order to solve the above problems, this paper first constructs a multi-queue, multi-server queuing model based on the server vacation and a multi-hop cascaded queuing model from the perspective of local intersections and global commuting paths. We analyze the theoretical changes in passage delay costs at local intersections and on global commuting paths as a function of traffic flow and the random duration of traffic signals. On this basis, this article proposes a collaborative intelligent traffic planning algorithm based on artificial intelligence, which utilizes traffic sensors to dynamically perceive traffic congestion status and collaboratively plans the optimal duration of traffic signals and the optimal driving path of vehicles from both local and global perspectives, thereby maximizing the on-time arrival ratio of vehicles while ensuring the required commuting delay. The simulation results show that the proposed method can increase the on-time arrival ratio of vehicles by at least 20% compared to contrast methods while meeting the requirements relating to commuting delays. This verifies that our method can provide support for the improvement in efficiency in future Internet of vehicles. [ABSTRACT FROM AUTHOR]

The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society's constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities' traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour. [ABSTRACT FROM AUTHOR]

This paper uses colored Petri nets (PN) to develop a model of vehicle movement in a city. The modeler defines the number and location of crossroads, the connections between them, and how many vehicles are at the given points of the network. The vehicles are recognized by their license plate numbers, and it is possible to determine where they start their journey and where they are going. The algorithm proposed in this paper suggests the shortest vehicle route based on Dijkstra's algorithm. This study focuses on improving route planning by considering road usage, which is determined by the starting and ending locations of vehicles (as if traffic cameras were identifying license plates). This approach will lead to optimal control of traffic lights (or vehicle navigation) to minimize traffic jams and make good use of all roads. Additionally, this paper shares the results of preliminary simulations using both colored and uncolored Petri nets in the GPenSIM environment. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios. [ABSTRACT FROM AUTHOR]

Developing appropriate signal timing strategies is a primary concern in traffic signal control; however, professionals are constrained by predefined controller options. Existing signal controllers in North America adhere to National Electrical Manufacturers Association (NEMA) standards with ring-barrier control (RBC) design. Due to unique traffic conditions and objectives for each traffic mode, conflicts arise, making it challenging to devise suitable traffic control strategies within current controllers. The limitations include inflexibility in defining pretimed signal diagrams, modifying them, and defining algorithms for modification. This paper introduces the event-based controller (EBC) framework, implemented in DUMKA_E software, capable of replicating NEMA-based RBC functionalities while providing additional flexibility beyond conventional RBC controllers. The EBC eliminates the asymmetry of flexibilities in existing traffic controller frameworks, addressing the challenges mentioned. Overcoming critical limitations of RBC, such as barrier constraints and restricted functionalities to built-in algorithms, the EBC empowers professionals to craft custom traffic signal control strategies. Unlike approaches enhancing RBC with complex computation, the EBC offers flexible functionalities without sacrificing simplicity. The study shows that the EBC's fundamental concepts are no more intricate than RBC's, positioning the EBC as a potential traffic controller framework. The paper establishes the EBC's capability to reproduce RBC controllers' core logic, showcasing advanced abilities for more efficient control logic than RBC. The EBC's functionalities enable context-sensitive traffic signal control strategies, catering to special conditions like multi-modal activities and diverse priority requests, highlighting its full potential. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a major problem in this rapidly growing world. Everyone operating a vehicle, as well as the traffic police in charge of managing the traffic, finds it difficult to become stuck in heavy traffic. For this a set, predetermined timing for traffic flow for each direction at the junction is utilized by traditional traffic light controllers. However, the concept of a fixed time traffic signal controller does not work well in places with uneven traffic. A dynamic traffic control system is therefore required, which regulates the traffic signals in accordance with the volume of traffic. This paper proposes a model that uses reinforcement learning (RL) along with deep neural networks (DNN) to manage discretions (signal status) for an environment with the help of Simulation of Urban MObility (SUMO). A simulation of real-world environment consisting a network of Four-way crossroad junction that contains 4 arriving lanes and 4 exiting lanes is used to train the agent. The main objective of this research study is to construct a model that can independently determine the best course of action and aims to provide better traffic management that will decrease the average waiting time, cause lower congestion, and provide a smooth flow of traffic. [ABSTRACT FROM AUTHOR]

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach. [ABSTRACT FROM AUTHOR]

The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to the rapid increase of the population in the world, traffic signal controlling and monitoring has become an important issue to be solved with regard to the direct relation between the number of populations and the cars' usage. In this regard, an intelligent traffic signaling with a rapid urbanization is required to prevent the traffic congestions, cost reduction, minimization in travel time, and CO2 emissions to atmosphere. This paper provides a comprehensive review of computer vision techniques for autonomic traffic control and monitoring. Moreover, recent published articles in four related topics including density estimation investigation, traffic sign detection and recognition, accident detection, and emergency vehicle detection are investigated. The conducted survey shows that there is no fair comparison and performance evaluation due to the large number of involved parameters in the abovementioned four topics which can control the traffic signal controlling system such as (computation time, dataset availability, and an accuracy). [ABSTRACT FROM AUTHOR]

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis. [ABSTRACT FROM AUTHOR]

One of the world's challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques can be used to improve road traffic signal control and reduce real-time traffic delays at intersections without the requirement for substantial infrastructure analysis. Long wait times and significant energy consumption are just two of the problems of the current traffic signal control system. To optimal efficiency, the traffic signal's duration must be dynamically changed to account for current traffic volume. To lessen congestion, the approach taken in this research focuses on modifying traffic signal time determined by the density of vehicles at the crossroads. The main purpose of this article is to demonstrate heavy traffic and emergency vehicle prioritization from all directions at the traffic intersection for a speedy passage. Using the Pygame tool, the proposed method in this study, which includes a mechanism for estimating traffic density and prioritization by counting vehicles at a traffic junction, is demonstrated. The vehicle throughput for the adaptive traffic light built using Pygame is compared with the vehicle pass rate for the adaptive traffic light built using Simulation of Urban Mobility (SUMO). The simulation results show that the adaptive traffic light built using Pygame achieves 90% throughput compared to the adaptive traffic light built using SUMO. A Two-Dimensional Convolutional Neural Network (2D-CNN) is implemented using Tensorflow for vehicle classification. The 2D-CNN model demonstrated 96% accuracy in classifying vehicles using the test dataset. Additionally, emergency vehicles, such as ambulances, are given priority for quick passing. [ABSTRACT FROM AUTHOR]

In urban areas, utilizing traffic lights to prioritize vehicles at the intersection is a solution to control traffic. Among the smart traffic light methods, the methods based on machine learning are particularly important due to their simplicity and performance. In this paper, Q-learning with deep neural network are combined and used in two different intersection models. The first one is an individual intersection, and the second one is two intersections that are connected and shared their actions. By using this method, the traffic light can make an intelligent decision at the intersection to reduce vehicle consumption time by managing the allocation of phases. The proposed smart traffic light is studied and simulated via SUMO. The results illustrated that compared to fixed-time traffic lights, the average queue time of each vehicle in different traffic scenarios has been reduced by 34% in the individual intersection. In the case of two intersections, awareness and communication between agents led to a 24% reduction in the queue time of all cars in the heavy traffic scenario. [ABSTRACT FROM AUTHOR]

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches. [ABSTRACT FROM AUTHOR]

A widely used technique for locating the position of a train during its journey involves electrical detection: on a section of track, the train closes ("shunts") a circuit dedicated to its location via its wheels and axles. The quality of the wheel–rail contact is therefore particularly important for signalling management and traffic control. In this paper, we show that the current flowing through the track circuit induces a permanent modification and a nonlinear, frequency-dependent behaviour of the electrical contact. We propose an analytical expression that describes the evolution of the measured voltage as a function of current and frequency for these nonlinear behaviours. This behavioural law was obtained using experimental measurements on a real train and could therefore be integrated into a global model describing the track system. [ABSTRACT FROM AUTHOR]

As a result of the rapid growth of the world population, traffic signaling systems for monitoring and controlling the roads have turned to be an important issue facing humanity. To effectively overcome this problem, an accurate method for congestion reduction on the roads should be used which has a direct relation between the population and the cars’ usage. Various approaches derived from deep and transfer learning have been investigated in this context. This research implemented an optimized transfer learning approach for densely connected convolutional neural network (DenseNet201) models for multiple classifications (non-emergency cars, ambulance, police, and firefighter). Due to the non-availability of public datasets, a customized dataset has been created. This paper aims to improve the performance accuracy of vehicle classification using certain preprocessing algorithms on the input images and testing various optimization methods. The performance accuracy of the proposed model is evaluated using k-folds cross-validation 20:80 for the test and training, respectively. The metrics which are used for comparison with other deep learning techniques are based on exactness, recall, accuracy and F1-score. Test outcomes specify that the proposed transfer model-based optimization outperforms alternative deep learning algorithms regarding vehicle accuracy in classifying and reaches 98.6%. [ABSTRACT FROM AUTHOR]

This paper discusses low-cost approaches capable of ranking traffic intersections for the purpose of signal re-timing. We extracted intersections that are comprised of multiple roads, defined by alphanumeric traffic message channel segment codes per international classification standards. Each of these road segments includes a variety of metrics, including congestion, planning time index, and bottleneck ranking information provided by the Regional Integrated Transportation Information System. Our first approach was to use a ranking formula to calculate intersection rankings using a score between 0 and 10 by considering data for different times of the day and different days of the week, weighting weekdays more heavily than weekends and morning and evening commute times more heavily than other times of day. The second method was to utilize unsupervised machine learning algorithms, primarily k-means clustering, to accomplish the intersection ranking task. We first approach this by checking the performance of basic k-means clustering on our data set. We then explore the ranking problem further by utilizing data provided by traffic professionals in the state of Tennessee. This exploration involves using MATLAB to minimize the mean-squared error of intersection rankings to determine the optimum weights in the ranking formula based on a city's professional data. We then attempted an optimization of our weights via a brute-force search approach to minimize the distance from ranking formula results to the clustering results. All the ranking information was aggregated into an online SQL database hosted by Amazon web services that utilized the PHP scripting language. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern in urban centers, as it can affect society, the environment, and the economy. There are many studies on the use of computational intelligence (CI) to improve mobility in urban centers. Some of these researches focus on developing strategies for traffic light programming, since traffic coordination is complex due to its many parameters, variables, and dynamic behavior, and also an inefficient traffic control plan can lead to increased delays and contribute to traffic congestion. Although there are many works in the literature on strategies for traffic control, there are still some contributions and gaps to be filled, especially because some studies do not consider the automatic optimization of traffic signals in real time, that is, according to the demand of vehicles on the roads, considering multiple objectives and the use of a network of intersections in their experiments. In addition, some of the proposed models are not independent of simulation to evaluate the solutions of CI algorithms, resulting in a more complex deployment in real situations. In this context, this paper presents a new method to optimize traffic light plan in a network of intersections and in real time, called Active Control of Traffic Signals (ACTS) associated with the Non-Dominated Sorting Genetic Algorithm, considering multiple objectives in the optimization process (minimizing the average delay time and the number of vehicles stops per cycle). To test the applicability of the model, a real dataset of vehicle demand collected by the Company of Transport and Traffic of Belo Horizonte (BHTrans) is loaded into the AIMSUN simulator, then the method is applied and compared with the current traffic control plan used by BHTrans. The results show that the ACTS method reduces the average vehicle delay by almost half compared to the results obtained with the current solution used by BHTrans. In real life, this means less time spent in traffic, which promotes faster traffic flow, reducing traffic congestions. [ABSTRACT FROM AUTHOR]

The paper presents a proposed queuing model based on Kendall's notation for the intersection of two streets in Panama City (53 East and 56 East). The proposed model is based on a set of traffic lights that controls the flow of vehicles at the intersection according to a predetermined schedule. The model analyzes the stability of the system and simulations are performed to evaluate its performance. The main objective of the paper is to optimize the vehicle flow by minimizing the waiting time for passage. In the study, it was observed that the current traffic light system on Calle 50 (50th Street) is unstable and oversaturated during weekdays, which generates large vehicle queues with no estimated exit times. It was proposed to increase the system capacity to 1300 vehicles per hour to achieve reasonable stability and provide a solution to improve traffic signal timing on 50th Street. The need to increase the system capacity has been demonstrated and an optimal value has been suggested. The evaluation of other models and the use of AI can further strengthen the system and improve the prediction accuracy in different traffic scenarios. [ABSTRACT FROM AUTHOR]

Using reinforcement learning to control traffic signal systems has been discussed in recent years, but most works focused on simple scenarios such as a single crossroads, and the methods aiming at large-scale traffic scenarios face long-time training and suboptimal results. In this work, we develop a new multi-agent reinforcement model for large-scale traffic signal control tasks, and a curriculum transfer learning method is developed to optimize the joint policy step by step. The policies for different intersections are trained in a partially observable Markov decision process with centralized training and decentralized execution mechanism, and we design transformer modules for both the policy and evaluation networks by attention mechanism. We first train policies in a simple traffic scenario, and then these policies are transferred to the next curriculum by policy reloading, while the experiences of the source task are reused selectively. With the number of agents increasing, our method can achieve satisfactory performances quickly by reusing the knowledge from previous curriculums. We conduct several experiments on the Cityflow testbed. In the case of more than 10 crossroads, our model improve the mean reward from 3.0 to 5.0. [ABSTRACT FROM AUTHOR]

Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes an experiment to develop and test machine learning models, i.e., long short-term memory, gated recurrent unit recurrent networks, and stacked autoencoder networks. It compares their effectiveness with traffic prediction results generated using a widely recognized traffic simulator that analyzes traffic at the level of individual vehicles. [ABSTRACT FROM AUTHOR]

Multi‐agent reinforcement learning has played an increasingly important role in intelligent traffic signal control due to its self‐learning ability. However, existing algorithms only focus on signal timing mechanism design while ignoring the exponential growth of the joint action dimension as the number of intersections increases, which will ultimately face the learning difficulty. In this paper, traditional traffic methods are introduced into MARL to flexibly determine the phase and duration of each intersection. The proposed MARL algorithm based on mean field theory has the ability to convert a large number of agents to approximately binary interaction, which can effectively reduce the dimension of joint action space in multi‐agent environment and learn in a robust process. Besides, to improve the performance of traditional traffic methods, the recurrent neural network (RNN) and an improved Webster's formula with revised parameters are combined to dynamically determine the phase duration according to the historical volume of traffic flow. The simulation results indicate that the proposed algorithm shows superior scalability compared to baseline methods and has great potential to be applied in the large scale road‐networks scenario. [ABSTRACT FROM AUTHOR]

This study presents design of traffic light system with feedback control that considers a crossroad in an urban area. Two types of controllers are designed – fuzzy and analytical, which have been tested separately on Aimsun platform through a simulation. The aim of the study is to compare the performance of both controllers in terms of increasing traffic flow and decreasing queue length. The controllers manage the duration of the green light according to the traffic flow. Two different formal models are designed, tested, and compared. They have produced adequate solutions in terms of developing controllers for modeling and simulation of transportation tasks. [ABSTRACT FROM AUTHOR]

The article presents the initial experience (spring-summer 2023) of using artificial neural networks (ANN) in improving traffic management in the large Russian city of Tyumen. Using the example of one of the intersections of the city's road network, it is shown how much transport delays are reduced when the duration of the traffic light cycle phases is quickly adjusted to the actual traffic intensity when compared with the usual previously used traffic light predictive mode. For the specific intersection of Odesskaya and Kotovskogo streets in Tyumen, considered in this article, the traffic light control mode using an ANN can significantly (by 20.6 ... 22.4%) reduce the average delay time of vehicles. It is also important that the reduction in traffic delays, which is possible with the regulation of traffic using ANN, helps to reduce stress for road users and improve road safety. The article presents historical data illustrating the dynamics of changes in the field of traffic management and road safety in Tyumen. This information well confirms the thesis about the dialectic of systemic development and the need for a gradual increase in the intellectual component in traffic management in large cities. The Applications (Appendix A and Appendix B) present the code of the auxiliary procedures and functions module and the code of the main data collection module used to optimize the traffic light control mode at the experimental intersection of the Tyumen road network. The main conclusion of the study is that use of an ANN allows taking into account a much larger number of factors and optimizing the control of the entire object, consisting of several intersections, which is not achievable using predictive modes and local adaptive control. [ABSTRACT FROM AUTHOR]

Platooning is generally known as a control method for driving a group of connected and automated vehicles in motorway context. Nevertheless, platoon control might also work on urban roads. One possible strategy to increase overall road traffic performance and to reduce congestion in urban traffic networks is to combine platooning with traffic signal control at intersections. The traffic flow can be maximized with coordinated scheduling of traffic signals together with platooning activities, resulting in decreased travel times and fuel consumption. This paper investigates several aspects of this combined control, such as the procedures for coordination and communication between platooning vehicles and traffic signals. Efficient algorithms are suggested to optimize platoon formation and dissolution at junctions and to change traffic signal phases depending on platoon arrival and departure times. The proposed solutions have been tested and verified with SUMO, a high-fidelity microscopic traffic simulator. [ABSTRACT FROM AUTHOR]

The real-time vehicular traffic system is an integral part of the urban vehicular traffic system, which provides effective traffic signal control for a large multifaceted traffic network and is a highly challenging distributed control problem. Coordinating vehicular traffic enables the network model to deliver an efficient service flow. Consider that there are four lanes of vehicular traffic in this situation, allowing parallel vehicle movements to occur without causing an accident. In this instance, the vehicular system's control parameters are time and vehicle volume. In this work, vehicular traffic flow is examined, and an algorithm to estimate vehicle waiting time in each direction is estimated. The effectiveness of the proposed vehicle traffic signal distribution control system by comparing the experimental results with a real-time vehicular traffic system is verified. This is also illustrated numerically. [ABSTRACT FROM AUTHOR]

We studied the ability of deep reinforcement learning and self-organizing approaches to adapt to dynamic complex systems, using the applied example of traffic signal control in a simulated urban environment. We highlight the general limitations of deep learning for control in complex systems, even when employing state-of-the-art meta-learning methods, and contrast it with self-organization-based methods. Accordingly, we argue that complex systems are a good and challenging study environment for developing and improving meta-learning approaches. At the same time, we point to the importance of baselines to which meta-learning methods can be compared and present a self-organizing analytic traffic signal control that outperforms state-of-the-art meta-learning in some scenarios. We also show that meta-learning methods outperform classical learning methods in our simulated environment (around 1.5–2× improvement, in most scenarios). Our conclusions are that, in order to develop effective meta-learning methods that are able to adapt to a variety of conditions, it is necessary to test them in demanding, complex settings (such as, for example, urban traffic control) and compare them against established methods. [ABSTRACT FROM AUTHOR]

Traffic planning and management is an important task for governments to achieve a proper functioning of the cities. The configuration of road signs and traffic lights is part of this process. The selection of an appropriate configuration is modeled as a variant of the traffic light cycle optimization problem (TLCOP), which is NP-Hard. To evaluate the impact of each configuration, agent-based simulations can be performed. Based on the importance and complexity of the problem, the objective of this paper is to present and evaluate a metaheuristic scheme to solve it and analyze solutions obtained. The algorithms were applied to six zones of Havana, Cuba. Among all the algorithms used, the genetic algorithm obtained the best performance. [ABSTRACT FROM AUTHOR]

The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach. [ABSTRACT FROM AUTHOR]

Deep Reinforcement Learning (DRL) has demonstrated its great potential for Adaptive Traffic Signal Control (ATSC) tasks at single‐intersection. In the transportation network multi‐agent environment, cooperative learning among multi‐agents has become a hot research topic. Based on the distributed control model, this paper presents a hybrid reward function model for the dynamic density method of intersections, which emphasizes the priority of emergency vehicles (EMV) while maximizing the traffic efficiency of social vehicles, and solves the problem of sparse reward due to the ambiguous guidance relationship between the multi‐agent Deep Reinforcement Learning (MDRL) state and reward function of the urban road network scenario. On the other hand, based on multi‐agent A2C (MA2C) algorithm, this paper presents Shared Experience MA2C (SEMA2C) between agents. In the transportation network, each intersection represented by an agent has similar task objectives. SMEA2C algorithm takes the current agent as the main body of self‐learning, and utilizes the principle of importance sampling to learn from the experience data of the agents located at adjacent intersections. The experimental results show that the proposed SEMA2C performs well in multi‐agent traffic signal control tasks, and has greater advantages than the similar algorithms. [ABSTRACT FROM AUTHOR]