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When tackling high-dimensional, continuous simulation-based optimization (SO) problems, it is important to balance exploration and exploitation. Most past SO research focuses on the enhancement of exploitation techniques. The exploration technique of an SO algorithm is often defined as a general-purpose sampling distribution, such as the uniform distribution, which is inefficient at searching high-dimensional spaces. This work is motivated by the formulation of exploration techniques that are suitable for large-scale transportation network problems and high-dimensional optimization problems. We formulate a sampling mechanism that combines inverse cumulative distribution function sampling with problem-specific structural information of the underlying transportation problem. The proposed sampling distribution assigns greater sampling probability to points with better expected performance as defined by an analytical network model. Validation experiments on a toy network illustrate that the proposed sampling distribution has important commonalities with the underlying and typically unknown true sampling distribution of the simulator. We study a high-dimensional traffic signal control case study of Midtown Manhattan in New York City. The results show that the use of the proposed sampling mechanism as part of an SO framework can help to efficiently identify solutions with good performance. Using the analytical information for exploration, regardless of whether it is used for exploitation, outperforms benchmarks that do not use it, including standard Bayesian optimization. Using the analytical information for exploration only yields solutions with similar performance than when the information is used for exploitation only, reducing the total compute times by 65%. This paper sheds light on the importance of developing suitable exploration techniques to enhance both the scalability and the compute efficiency of general-purpose SO algorithms. Funding: T. Tay thanks the Agency for Science, Technology and Research (A*STAR) Singapore for funding his work. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0110. [ABSTRACT FROM AUTHOR]

Traffic congestion is a serious problem in the US, and traffic signal control is one of the effective solutions to congestion. Previous research on model predictive control (MPC)-based traffic signal control showed substantial benefits over conventional methods. This study focused on implementing MPC over a large-scale network with complex intersections and the impact of cycle length, network size, and imperfect state estimation on performances. This study implemented a virtual phase link (VPL)-based model predictive control method which used the number of vehicles in each VPL as input state variables and was suitable for National Electrical Manufacturing Association (NEMA) ring-barrier control. To test the impact of network size, the performance of distributed MPC (36 intersections in the network are divided into five subnetworks) was compared with that of MPC over the full network for a set of cycle lengths. To test the impact of imperfect state estimation, we synthetically infused estimation error and developed two scenarios, MPC-error and MPC-error narrow, which had higher and lower estimation errors, respectively. The performance of these MPC methods was compared with that of the existing time-of-day (TOD) method and an offline method that used Webster's method for split and MULTIBAND for cycle length and offset optimization. Trajectory and linkwise signal performance measures were collected from the simulation to evaluate performance. The distributed MPC method with perfect state estimation had the lowest delay and highest energy efficiency of all the methods. The performance of MPC decreased as the prediction inaccuracy increased. MPC-error had 7% and 11% more delay than MPC-error narrow in the morning and evening peaks, respectively. Overall, simulation results suggest that even with imperfect state estimation, MPC methods will outperform offline methods significantly. [ABSTRACT FROM AUTHOR]

Nowadays, traffic congestion poses a significant problem in urban areas, affecting daily life. The imbalance between supply and demand causes overloading. Intersections are critical nodes in urban transport networks, and their design has a substantial impact on traffic safety and efficiency. Microscopic traffic flow models, which describe vehicle maneuvers and traffic patterns, are widely used for optimal design and performance evaluation of transportation facilities. This article focuses on traffic modeling at intersections using the Aimsun software. The examined intersections are located in Topoľčany, Slovakia, where a 13-h traffic survey was conducted on Tovarnická Street. In addition to the current situation, simulations were performed for scenarios distributing 10% and 20% of peak traffic intensity over longer periods, with overall quality levels determined. This article highlights the importance of researching intersection throughput and the need for capacity assessment in planning any construction that affects traffic in the area. [ABSTRACT FROM AUTHOR]

Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments. [ABSTRACT FROM AUTHOR]

This study aims to devise a traffic electronic information signal acquisition system employing Internet of Things and artificial intelligence technologies, offering a novel approach to address prevailing challenges related to traffic congestion and safety. Initially, the hardware circuit for the high-speed signal acquisition control core is developed, leveraging Field-Programmable Gate Array technology. This facilitates wireless monitoring of signal acquisition. Subsequently, a comprehensive time signal acquisition system is formulated, encompassing modules for communication, acquisition, storage, adaptive measurement, and signal analysis. The geomagnetic acquisition module within this system is utilized for collecting geomagnetic signals, which are then translated into switch signals indicating the presence or absence of vehicles. These signals are subsequently transmitted to the geomagnetic signal processor. Experimental results pertaining to the signal acquisition system reveal a notable peak storage speed of 200KB/s, considering the utilization of one million sampling points. Across a series of tests, the maximum relative error of the obtained results ranges from 2.2% to 2.7%, underscoring the consistency and reliability of the measurements. In comparison to existing testing devices, the system exhibits heightened accuracy in test results, rendering it more apt for traffic signal acquisition applications. In conclusion, this study accomplishes the collection and dissemination of diverse traffic information, furnishing robust support for traffic control and ensuring safe operations. [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]

Recent advancements in the industrial revolution and artificial intelligence have aided in the development of novel approaches that have considerable potential for increasing the efficiency of traffic networks. Emerging concepts of autonomous driving and machine learning can be incorporated intelligently for improving traffic operations and control. In the coming years, traffic composition can vary in terms of autonomous vehicle (AV) penetration rate, which can lead to a heterogeneous traffic environment. Traffic control methods for such complex networks need to be designed effectively for accommodating the positive effects of AV implementation without compromising the safety and level of service in the presence of regular vehicles (RVs). An intelligent-based optimization framework for traffic signal control under heterogeneous AV-based traffic is proposed in this paper. This framework utilizes state-of-the-art machine-learning approaches to represent and design different components of the optimization process of cycle length. Further, SHapley Additive exPlanations (SHAP) is used to enhance model interpretability. The proposed optimization framework improves performance under congested traffic conditions compared with that of the conventional optimization methods. Compared to pure RV-based traffic, the penetration rates of 25%, 50%, and 100% can decrease optimized cycle lengths by 26%, 39%, and 53%, respectively, which can result in delay reductions of approximately 18%, 31%, and 56%, respectively. The proposed framework when applied in an adaptive-based manner can help in the generalization of controlling existing signalized intersections with the gradual penetration of AVs without the extra infrastructure or specific operational and connectivity capabilities of the involved vehicles. [ABSTRACT FROM AUTHOR]

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]

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators. [ABSTRACT FROM AUTHOR]

Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections. [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]

Decentralized traffic signal controls, such as max-pressure (MP) control, also known as back-pressure (BP) control, have received increased attention recently. MP signal control has been analytically proven to maximize the network throughput and stabilize vehicle queue lengths whenever possible. However, previous work on MP signal control with cyclic and noncyclic phases did not include pedestrian access, which may increase pedestrians’ travel time, and delay or even encourage some dangerous behaviors like jaywalking. Because the movement of pedestrians is a nonnegligible factor in traffic management, and many urban planning researchers have found that walking space and walking continuously have significant health, safety, and environmental impacts, a pedestrian-friendly MP signal control policy is needed. Here, we propose a novel pedestrian-friendly MP signal control, Ped-MP, that considers pedestrian access in an urban network to achieve both maximum stability for private vehicles and a comfortable, safe walking experience. This study modifies the original MP control to include pedestrians’ access for the first time. Furthermore, this policy still inherits the decentralized property of original MP control, which means it only relies on the local information of individual intersections. Simulation studies are implemented on a popular benchmark network, the Sioux Falls network, with added pedestrians network. The results indicate that, although considering pedestrians’ access may reduce the stable region for vehicles, the pedestrians’ travel time and delay can be reduced significantly. [ABSTRACT FROM AUTHOR]

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the resilience degradation caused by traffic accidents and developing relevant resilience optimization strategies. A two-stage accident resilience triangle framework was proposed by comparing the differences between natural disasters and traffic accidents. To maximize system resilience, a network-wide traffic signal optimization model was presented. Spillback constraints and equilibrium constraints were established to enhance the capacity of urban-road networks to minimize congestion escalation, in addition to rapid recovery. A two-level algorithm based on greedy strategy and gradient descent was designed to solve the proposed non-linear programming model. In the experiment, a virtual road network was constructed based on the Simulation of Urban Mobility (SUMO) platform for validation and sensitivity analysis. The experimental results revealed that: (1) Compared to the traditional resilience framework, the proposed two-stage accident resilience framework can more reasonably describe the change mechanism of road network resilience under disturbance. (2) The proposed resilience-based traffic signal optimization model improved the system resilience under different conditions of traffic demand, accident severity, and rescue time in terms of the maximum performance degradation and recovery time. Precisely, the resilience loss is reduced by a maximum of 1.4%. Finally, the proposed model was further implemented with field data. The resilience improvement was significant during the evening rush hour. The results of this study contribute toward transportation resilience research and accident rescue strategies with respect to traffic management and control. [ABSTRACT FROM AUTHOR]

The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

In current signal control schemes, the prevailing control method is still the arterial coordinated control, which involves coordinating a group of adjacent traffic signals along a major road. However, during the actual implementation process, technicians have observed substantial variations in the effectiveness of mainline coordinated control across different road segments. These variations are attributed to different signal control strategies, giving rise to an ongoing debate regarding the alignment of the beginning or end of the green phase. The impact of these two alignment methods on bandwidth and road capacity in arterial coordinated control remains a subject of controversy. Therefore, this paper aims to compare the two methods using a real-world scenario. Based on the inherent characteristics of coordinated control, a traffic capacity model for coordinated segments was established. Subsequently, the influence of green start alignment and green end alignment on the capacity of green wave segments and the improvement of existing traffic conditions was analyzed using the proposed capacity model. To validate the analytical model, simulations were executed using VISSIM, and data on delay and the number of stops were collected. The conclusion indicates that from the perspective of benefits and safety, green start alignment outperforms green end alignment. This research provides insights for parameter settings in signal coordinated control, with the potential to significantly reduce the number of vehicle stops, and has practical significance for energy conservation and emission reductions. [ABSTRACT FROM AUTHOR]

As population increases, one of the factors affecting life is traffic. Efficient traffic management has a direct positive impact on issues such as time, carbon dioxide emissions, and fuel consumption. Today, an important parameter under the heading of traffic is the signalling systems for intersections, which are operated with fixed-time, semiactuated, fully actuated, and fully adaptive control methods. In this study, an adaptive traffic management model is developed for signalised intersections. The adaptive traffic management model developed includes phase extension with minimum and maximum time intervals dependent on density and phase skip features. Additionally, the most distinctive feature of the model is its flexible phase structure rather than a sequential phase. The Heybe intersection, located within the boundaries of Antalya province, is modelled one-to-one in the simulation of urban mobility (SUMO) simulation programme with real intersection data. The developed adaptive traffic management model is applied to the Heybe intersection, and the effects of the model are revealed. Improvements obtained from the SUMO simulation programme were verified through visual inspection, and high-accuracy results were determined. As a result of the studies, it was found that the application of the adaptive traffic management model developed at Heybe intersection, which has approximately 50,000 vehicles passing daily, resulted in a 27.2% improvement in the average delay per vehicle parameter, a 32.4% improvement in the average waiting time per vehicle parameter, and a 16.7 % improvement in the average speed per vehicle parameter. [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]

Emergency vehicles (EVs) must reach their destination within the shortest amount of time considering the importance of their service in saving lives and reducing damage to property. The aim of this research was to reduce the delay experienced by these vehicles through the alteration of traffic control signals to give preferential treatment to EVs upon detection. Specifically, this study compared three algorithms with concepts of EV pre-emption, longest queue first and emergency vehicle signal priority (EVSP) based on average general and EV delays through a traffic simulation approach using the Vissim software program and its component object model interface. The performance evaluation was applied at Tuk Chai intersection in Bangkok, Thailand as a case study. The second algorithm of longest queue first with pre-emption was found to be the best approach for both peak hour traffic conditions and 0.8 (peak) traffic conditions (i.e. peak hour traffic volume reduced by 20%), performing better than the do-nothing condition by 69.70% for stochastic EV input and 79.14% for fixed EV input. The findings also show that the algorithm of preferential treatment using EVSP strategies produced a competitive and robust performance with EV delay values better than the do-nothing condition by 75.96, 77.27 and 45.67% in 0.8 (peak), peak hour and 1.2 (peak) traffic conditions, respectively (the last of these three being peak hour traffic volume increased by 20%). [ABSTRACT FROM AUTHOR]

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles' origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% precision, on average. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an average error rate of 3.065% in terms of origin and destination counts. [ABSTRACT FROM AUTHOR]

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. Increasing the capacity of road networks is not always possible, thus a more efficient use of the available transportation infrastructure is required. Another issue is that many problems in traffic management and control are inherently decentralized and/or require adaptation to the traffic situation. Hence, there is a close relationship to multiagent reinforcement learning. However, using reinforcement learning poses the challenge that the state space is normally large and continuous, thus it is necessary to find appropriate schemes to deal with discretization of the state space. To address these issues, a multiagent system with agents learning independently via a learning algorithm was proposed, which is based on estimating Q-values from k-nearest neighbors. In the present paper, we extend this approach and include transfer of experiences among the agents, especially when an agent does not have a good set of k experiences. We deal with traffic signal control, running experiments on a traffic network in which we vary the traffic situation along time, and compare our approach to two baselines (one involving reinforcement learning and one based on fixed times). Our results show that the extended method pays off when an agent returns to an already experienced traffic situation. [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]

Aiming at the problems of multi-AGV (automated guided vehicle) path planning in intelligent warehouse, such as poor real-time performance, weak obstacle recognition ability, multi-AGV collision, deadlock and congestion, a collaborative control method for intelligent warehouse traffic signals control and multi-AGV path planning is proposed. Traffic signals and multi-AGV path planning are regarded as a whole in this method. A collaborative control framework for traffic signals and multi-AGV path planning is designed. The LS-A3C (long short-asynchronous advantage actor-critic) algorithm and Bi-LSTM-CBAM (bi-long short-term memory-convolutional block attention module) algorithm are proposed as the core algorithm of the framework. The long-term and short-term information of traffic signals are encoded in LS-A3C algorithm, which uses a long short-term encoder and attention mechanism. They are represented by learning cell features. The A3C framework is used to calculate the Q value of the cell and the control strategy. The traffic signals time adapting to AGV flow is adjusted to solve the problems of multi-AGV collision, deadlock and congestion. The output result is spliced by calculating the state characteristics of the present moment and the leading moment in Bi-LSTM-CBAM algorithm to solve the problem of gradient disappearance and explosion in neural network effectively and improve real-time AGV path planning. The attention mechanism module CBAM is introduced. Weights are assigned based on how important the input is in order to strengthen the AGV's ability to identify obstacles. Finally, simulation experiments are carried out on Sumo and Gazebo joint simulation platform. The experimental results show that the collaborative control method significantly reduces the AGV collision, deadlock and congestion, significantly improves the obstacle recognition ability, and greatly enhances the real-time path planning. The purpose of improving the AGV operation efficiency is achieved. [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]

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles' origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% precision, on average. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an average error rate of 3.065% in terms of origin and destination counts. [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]

In this paper is present the procedure for introducing the coordination of light signals at five intersections on the Boulevard "St. Kliment Ohridski" in Skopje. The primary purpose of a traffic signal is to assign the right-of-way to intersecting traffic streams for the purpose of ensuring that all streams are served safely and without excessive delay. A properly designed and timed signalized intersection will minimize fuel consumption, delay, and stops without having an adverse effect on safety. Travelers will realize one or more of the following benefits at intersections where the traffic signal is needed, properly designed, and well timed: orderly movement of traffic, increase in the traffic-carrying capacity of the intersection, reduction in the frequency and severity of certain types of crashes, progressed traffic when traveling in a coordinated signal system, and interruption of heavy traffic flow to provide safe opportunities for minor movements. Coordinated signal timing synchronizes traffic movements and manages the progression speed of specific modes where uninterrupted flow is desired along a corridor. While traditionally applied to increase vehicular traffic flow and reduce peak-hour delay, coordinated signal timing can also be optimized for slower speeds, creating an uninterrupted flow for bicyclists or low vehicle progression speeds for a pedestrian-friendly downtown. Signals may also be timed to coordinate transit headways along routes where regular transit service is consistent and has low variability. Coordination of traffic signals can reduce the number of stops along a corridor and provide for a continuous flow of traffic at the target speed. Progression speeds should be set at or below the target speed, rather than existing 85th percentile speeds. [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]

In recent years, high-resolution traffic signal event data has provided valuable insights into understanding and managing congestion at signalized intersections. While existing applications primarily employ automated traffic signal performance monitoring (ATSPM) systems as postanalysis tools for identifying everyday congestion causes, traffic engineers are increasingly overwhelmed by the number of ATSPM-capable intersections. The workload increases extensively as the number of ATSPM-capable intersections rises mainly due to the necessity of manually checking and generating performance figures. Nonetheless, an advanced ATSPM system capable of automatically detecting time-of-day congestion bottlenecks among multiple intersections and suggesting "top intersections of interest" would significantly aid traffic managers in monitoring historical congestion and preventing future congestion occurrences. This paper introduces an efficient graphical automated congestion ranking method for capable intersections, leveraging high-resolution traffic signal event data as the basis for automated congestion ranking. To accomplish these objectives, we build upon ATSPM concepts by continuously generating ATSPM measures of effectiveness (MOEs). Utilizing continuously generated ATSPM performance measures in Frisco, Texas, over several months, we devise an efficient graphical method for ranking hourly congestion levels among the studied ATSPM-capable intersections. All intersections are assessed and ranked using a multiobjective optimization technique, the Pareto front method. The points on the Pareto front represent dominating intersections with at least one inferior performance measurement, warranting prioritized improvement. The dominating points identified from the test dataset were validated and further explained using Purdue coordination diagrams (PCD), along with another individual dataset--Wejo-connected vehicle data. The outcomes of this approach have proven the validity of the method. [ABSTRACT FROM AUTHOR]

When a work zone is located at an intersection, it greatly reduces the capacity and change the traffic flow characteristics. However, the impacts of work zones have not attracted much attention, and the signal control method of the intersection with a work zone has not been investigated yet. This paper focuses on a specific type of work zone, which is located within the area of an intersection. The saturation flow rate model, traffic wave model, traffic delay model, and emptying time model are proposed to capture the traffic flow characteristics of the intersection with an island work zone, and a signal control model is further put forward. The real data is collected to calibrate, validate, and evaluate the proposed models. The results indicate that the proposed signal control model can reduce about 15.6% of queue length and 17.2% of traffic delay for the intersection with an island work zone. [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]

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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]

Services for AI tasks have garnered a lot of attention as an integral aspect of intelligent services in the new era. However, implementing such a system in a stable and distributed manner, while simultaneously coordinating the use of cloud computing and remote edge devices, is challenging due to the pressing need for energy and computing resources. The primary contribution of this study lies in the development of a distributed co-inference architecture that harnesses the collective intelligence of interconnected agents to optimize traffic flow in real-time. By combining Q-learning with enhanced deep learning, our approach enables traffic signals and routing decisions to adapt dynamically to changing traffic patterns and environmental conditions. The security, responsiveness, and dependability of intelligent systems deployed close to end-users are improved by deploying deep learning systems. Another critical aspect where latency and accuracy in models are traded off is deep learning model optimization. Finding the best offloading policy and model for deep learning services requires an end-to-end decision-making solution that takes into account computation-communication problems. This study presents a holistic network optimization approach for scheduling AI services based on artificial intelligence. By adjusting for differences in computational resources and network congestion, the suggested deep Q-learning technique maximizes the throughput of AI tasks in general. This research introduces a virtual queue for analyzing the system's Lyapunov stability and employs a multi-hop Directed Acyclic Graph (DAG) to explain Q-learning of Reinforcement learning-based co-inference network topology. To optimize the total task processing rate, the study develops an Optimized self-adaptive glow worm swarm optimization method (SA-GSO) based on deep Q-learning. It then proposes a prioritybased data forwarding approach for efficiency. The study concludes by simulating the distributed co-inference system's platform. We attest to the superiority of our idea by comparing it to other standards. [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]

In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning, many single-agent deep reinforcement learning algorithms have been extended to multi-agent scenarios. Overestimation in value estimation of Q-learning is a significant issue that has been studied comprehensively in the single-agent domains, but rarely in multi-agent reinforcement learning. In this paper, we first demonstrate that Q-learning-based multi-agent reinforcement learning (MARL) methods generally have notably serious overestimation issues, which cannot be alleviated by current methods. To tackle this problem, we introduce the double critic networks structure and the delayed policy update to Q-learning-based multi-agent MARL methods, which reduce the overestimation and enhance the quality of policy updating. To demonstrate the versatility of our proposed method, we select several Q-learning based MARL methods and evaluate them on several multi-agent tasks on the multi-agent particle environment and SMAC. Experimental results demonstrate that the proposed method can avoid the overestimation problem and significantly improve performance. Besides, application in the Traffic Signal Control verifies the feasibility of applying the proposed method in real-world scenarios. [ABSTRACT FROM AUTHOR]