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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]

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]

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]

An alternative approach for real-time network-wide traffic control in cities that has recently gained attention is perimeter flow control. Many studies have shown that this method is more efficient than state-of-the-art adaptive signal control strategies for heterogeneously congested urban networks. The basic concept of such an approach is to partition heterogeneous cities into a small number of homogeneous regions (zones) and apply perimeter control to the interregional flows along the boundaries between regions. The transferring flows are controlled at the traffic intersections located at the borders between regions so as to distribute the congestion in an optimal way and minimize the total delay of the system. The focus of current work is the mathematical formulation of the original nonlinear problem in a linear parameter-varying (LPV) form so that optimal control can be applied in a (rolling horizon) model predictive concept. This work presents the mathematical analysis of the optimal control problem as well as the approximations and simplifications that are assumed in order to derive the formulation of a linear optimization problem. Numerical simulation results for the case of a macroscopic environment (plant) are presented in order to demonstrate the efficiency of the proposed approach. Results for the closed-loop model predictive control scheme are presented for the nonlinear case, which is used as "benchmark," as well as the linear case. Furthermore, the developed scheme is applied to a large-scale microsimulation of a European city with more than 500 signalized intersections in order to better investigate its applicability to real-life conditions. The simulation experiments demonstrate the effectiveness of the scheme compared with fixed-time control because all of the performance indicators are significantly improved. Funding: This work was supported by Dit4Tram (Distributed Intelligence & Technology for Traffic & Mobility Management) project from the European Union's Horizon 2020 research and innovation programme under [Grant 953783]. [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]

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios. [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]

Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver's behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users. [ABSTRACT FROM AUTHOR]

The rapid global spread of the coronavirus disease 2019 (COVID-19) is now a reality. China has taken urban traffic control measures to prevent and control the epidemic, but this has prevented the flow of people between cities. This study investigates the mechanism of the impact of urban traffic control measures on the intercity population flow in China using the one-way causal measurement method. The results show that the impact of urban traffic control measures on the intercity flow of the population changes with time. Based on this, this study makes scientific suggestions for the government on how to reasonably undertake traffic control measures. [ABSTRACT FROM AUTHOR]

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management. [ABSTRACT FROM AUTHOR]

The connected and autonomous vehicle (CAV) is promised to ease congestion in the future with the rapid development of related technologies in recent years. To explore the characteristics of mixed-traffic flow and the dynamic transmission mechanism, this paper firstly detailed the car-following model of different vehicle types, establishing the fundamental diagram of the mixed-traffic flow through considering the different penetration rates and fleet size of CAV. Secondly, this paper constructed the lane-changing judgment mechanism based on the random utility theory. Finally, the paper proposed a lane-level dynamic cell transmission process, combined with a lane-changing strategy and cell transmission model. The effectiveness and feasibility of the model are verified using simulation analysis. This model makes a systematic, theoretical analysis from the perspective of the internal operation mechanism of traffic flow, and the lane-level traffic strategy provides a theoretical basis for balancing urban lane distribution and intelligent traffic management and control. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods to control traffic congestion. However, most of the existing methods of traffic prediction focus on urban road scenarios, neglecting the complexity of multivariate auxiliary information in highways. Moreover, these methods have difficulty explaining the prediction results based only on the historical traffic flow sequence. To tackle these problems, we propose a novel traffic prediction model, namely Multi-variate and Multi-horizon prediction based on Long Short-Term Memory (MMLSTM). MMLSTM can effectively incorporate auxiliary information, such as weather and time, based on a strategy of multi-horizon time spans to improve the prediction performance. Specifically, we first exploit a multi-horizon bidirectional LSTM model for fusing the multivariate auxiliary information in different time spans. Then, we combine an attention mechanism and multi-layer perceptron to conduct the traffic prediction. Furthermore, we can use the information of multivariate (weather and time) to provide interpretability to manage the model. Comprehensive experiments are conducted on Hangst and Metr-la datasets, and MMLSTM achieves better performance than baselines on traffic prediction tasks. [ABSTRACT FROM AUTHOR]

Cooperative control of vehicle trajectories and traffic signal phases is a promising approach to improving the efficiency and safety of transportation systems. This type of traffic flow control refers to the coordination and optimization of vehicle trajectories and traffic signal phases to reduce congestion, travel time, and fuel consumption. In this paper, we propose a cooperative control method that combines a model predictive control algorithm for adaptive traffic signal control and a trajectory construction algorithm. For traffic signal phase selection, the proposed modification of the adaptive traffic signal control algorithm combines the travel time obtained using either the vehicle trajectory or a deep neural network model and stop delays. The vehicle trajectory construction algorithm takes into account the predicted traffic signal phase to achieve cooperative control. To evaluate the method performance, numerical experiments have been conducted for three real-world scenarios in the SUMO simulation package. The experimental results show that the proposed cooperative control method can reduce the average fuel consumption by 1% to 4.2%, the average travel time by 1% to 5.3%, and the average stop delays to 27% for different simulation scenarios compared to the baseline methods. [ABSTRACT FROM AUTHOR]

In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi'an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion. [ABSTRACT FROM AUTHOR]

An optimization model for traffic light control in an urban network of intersections is derived. The model is based on store-and-forward analytic relations, which account for the length of the queue of waiting vehicles in front of the traffic light intersection. The model is complicated with probabilistic relations that formalize the requirements for maintaining short queues of vehicles. Probabilistic inequalities apply to each intersection of the city network. Approximations of probability inequalities are given in the article. Quadratic deterministic inequalities, which are part of the set of the traffic flow control optimization problem, are derived. Numerical simulations are performed, applying mean estimated data for real traffic in an urban area of Sofia. The model predictive approach is applied to traffic light optimization and control. Empirical results give advantages of the obtained model compared to the classical store-and-forward optimization model for the total number of vehicles waiting in the considered urban network. [ABSTRACT FROM AUTHOR]

Traffic patterns in urban areas present a complex and dynamic system that is characterized by inherent uncertainties. The presented study is a traffic light control system with feedback. The controller of the system is designed in a fuzzy and conventional way and is applied to a network of two junctions. The verification is performed using the MATLAB fuzzy toolbox platform (for designing the fuzzy controller) and Aimsun platform for microsimulation of the two junctions using the two types of controllers. To accomplish the control of the system a fuzzy controller on heuristic rules proposed to allow adaptive traffic control on signalized junctions in urban environments. The Fuzzy Toolbox in MATLAB is used to simulate the fuzzy controller. The Aimsun traffic simulator is used to model and simulate a traffic network of two intersections. The green light duration in the Aimsun model is based on the results for the two controllers from two separated experiments. Simulations of Aimsun models with the two types of controllers, the fuzzy and the conventional one, are compared. The experiment is performed under the premise that the traffic flow is oversaturated. Findings show that in a network of two junctions both controllers perform in a similar manner for the first junction. However, for the second junction, the fuzzy controller tends to have some advantages over the conventional controller with regard to higher traffic flow. In conclusion, the overall performance of the fuzzy controller is better than the one of the conventional controller, but further research is needed for more complex traffic networks. [ABSTRACT FROM AUTHOR]

Observability and controllability are two critical requirements for a partially observable transportation system. This paper proposes a stepwise signal optimization framework with connected vehicle (CV) data as input to solve both challenges. First, a Bayesian deduction method based on low‐penetration CV data is established to estimate the traffic volume. Thereafter, an offline signal optimization model is constructed to simultaneously optimize the flexible lane settings and signal timings, which are set as the prior information for the third step. In the third step, an online deep recurrent Q‐learning (DRQN) signal optimization model dynamically adjusts signal settings based on real‐time traffic information. Numerical experiments demonstrate that the model outperforms the actuated control, the online DQRN model without offline filter, and the back‐pressure model by 9%–66% and 7%–29% in two networks. This study innovatively combines traffic state estimation and traffic signal control as an integrated process. It contributes to an improved understanding of traffic control in a CV environment and lays a solid foundation for future traffic control strategies. [ABSTRACT FROM AUTHOR]

Strategic traffic management is crucial for combating traffic congestion at the macroscopic level. However, such a field is still relatively unexplored, particularly for microscopic control objects, such as intersections and coordinated intersection groups. This article proposes a human-in-the-loop recommendation system for strategic urban traffic management, which follows an agent-based structure. A regional agent dispatcher is defined to assign agents for operation whenever “operation on-demand” is required. Such a requirement is identified by a daily-dependent operational mode on strategic traffic operations at a control object level. The strategic management scheme for each control object is guided by a strategic agent (customized), which is essentially a deep recommender model with a specific architecture. By featuring the multiagent design, a customized operational scheme can be generated at the intersection level, which instructs the corresponding controller to take specific operations. The utility of the recommendation system is demonstrated via a case study using real-world traffic data. In both offline and online evaluations, the system performs consistently at traffic operational recommendations in different scenarios and has the potential to provide more reasonable traffic operational strategies than a human-operated system. [ABSTRACT FROM AUTHOR]

Adaptive traffic signal control (ATSC) is an effective method to reduce traffic congestion in modern urban areas. Many studies adopted various approaches to adjust traffic signal plans according to real-time traffic in response to demand fluctuations to improve urban network performance (e.g., minimise delay). Recently, learning-based methods such as reinforcement learning (RL) have achieved promising results in signal plan optimisation. However, adopting these self-learning techniques in future traffic environments in the presence of connected and automated vehicles (CAVs) remains largely an open challenge. This study develops a real-time RL-based adaptive traffic signal control that optimises a signal plan to minimise the total queue length while allowing the CAVs to adjust their speed based on a fixed timing strategy to decrease total stop delays. The highlight of this work is combining a speed guidance system with a reinforcement learning-based traffic signal control. Two different performance measures are implemented to minimise total queue length and total stop delays. Results indicate that the proposed method outperforms a fixed timing plan (with optimal speed advisory in a CAV environment) and traditional actuated control, in terms of average stop delay of vehicle and queue length, particularly under saturated and oversaturated conditions. [ABSTRACT FROM AUTHOR]

Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model's technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies. [ABSTRACT FROM AUTHOR]

Cities need to develop, people's lives need to be improved, and the living environment needs to be enhanced. With the strengthening of the national policy of paying attention to people's livelihood, the speed of development, the increase of the number of cars, and the major demolition and construction of streets are in full swing everywhere. However, traffic congestion has become a serious social problem, which greatly affects economic development and people's daily life. Therefore, the study of signal coordination control of urban traffic arteries has become an urgent problem for all countries. In this paper, after a brief introduction of the basic concepts and related technologies of traffic control systems, we focus on the arterial coordinated control system. The adaptive control model studied in this paper reveals the vehicle arrival dynamics from a microscopic perspective, which helps to improve the adaptive control effect and alleviate urban intersection congestion. The use of traffic information acquisition and control systems can promote the application of vehicle networking technology, while moreover helping to provide important model simulations for future street simulation renovation. [ABSTRACT FROM AUTHOR]

In the domain of complex urban traffic networks, real-time Traffic Signal Control (TSC) serves as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple intersections involves considerable complexity. Deep Reinforcement Learning (DRL) has emerged as a robust solution. In recent years, there has been rapid advancement in TSC methods, with numerous researchers employing various novel DRL methodologies. Yet, existing surveys lack timeliness and universality in capturing the latest research. There is a notable gap in current research surveys with respect to the latest developments in TSC. Therefore, the focus of this paper lies in analyzing the most recent papers from the past five years, with the aim to provide a comprehensive and multi-dimensional review of the evolution of DRL in TSC. The survey categorizes current research based on model setups, utilized algorithms, and application scenarios. Finally, this paper highlights potential directions for future TSC research. [ABSTRACT FROM AUTHOR]

Modeling and control of road traffic in large-scale urban networks present considerable challenges. The traffic equilibrium phenomena, with the question of route choice behavior in case of heterogeneous urban networks, has not been thoroughly investigated in parsimonious and classical models due to the limitation, like large network size, spatiotemporal propagation of congestion, and the interaction between driver decisions, etc. In this paper, we propose a bi-level approximating dynamic equilibrium model (BLADEM) for the approximating dynamic equilibrium analysis in multi-region network based on macroscopic fundamental diagram (MFD). The proposed model combines the region-based model and the internal-region model. With the information from region MFD, the region-based model is used to implement the time-dependent regional route choice estimation. Traffic equilibrium condition (dynamic user equilibrium, DUE) is considered in an internal-region model with time-aggregated regional OD demand from the region level. Furthermore, the complexity of the proposed model is derived. Then, the comparative analysis of the algorithm complexity between the proposed model and the DUE model is given. The proposed model is evaluated based on the high-resolution vehicle trajectory data (or connected vehicles trajectory data) from the DiDi platform collected in Chengdu, China with more than 3,000,000 GPS points during a typical workday. The evaluation results show that the proposed model can obtain the approximating traffic dynamics compared with the DUE algorithm. Pleasantly, the improved calculation efficiency is between 21% and 42%. The results indicate the promising potential of using the proposed model to analyze approximating dynamic equilibrium in the multi-region heterogeneous network. [ABSTRACT FROM AUTHOR]

With the steadily growing traffic demand, urban traffic congestion is becoming a critical issue threatening several factors, including public safety, emissions of greenhouse gas, and transport inefficiencies. Thus, intelligent transport systems (ITS) have emerged as a promising solution to easing the burden of congestion. ITS rely on different technologies such as VANET (Vehicular Adhoc Networks) which provide the transportation system with ubiquitous connectivity allowing the exchange of traffic information between vehicles and roadside terminals. This can support numerous smart mobility applications such as traffic signal control and real-time traffic management. Hence, mobility models were developed to emulate and forecast the distribution of traffic which will be helpful to the design and management of traffic control strategies. In this context, this study specifically concentrates on developing a mobility model that reflects vehicular activities in urban environments based on vehicular information collected using vehicular communications. The behavior of vehicles along multi-lane roads and intersections is modeled as a stochastic process using queuing theory. Particularly, the queue system is analyzed as a continuous-time Markov chain (CTMC) and by calculating the steady-state probabilities, different performance measures are derived and analyzed under various scenarios. To validate the model, the obtained forecasts are compared with a queue model and realistic traces. The results show that the model is capable of reproducing the realistic behavior of traffic in urban roads without incurring heavy costs and time-consuming computing. The obtained estimates were then used to design an actuated traffic light and a vehicle speed adaptor. From the simulation results, it is clear that using the proposed traffic forecasting model helps reduce vehicles idling and travel times. • Using real-time traffic data collected via V2X increases prediction accuracy. • Markov chains allow realistic, fast and efficient urban traffic prediction. • CTMC traffic model shows better prediction accuracy compared to basic queue models. • Traffic prediction improves the performance of traffic-aware ITS applications. [ABSTRACT FROM AUTHOR]

Due to the nonlinearity and dynamics of transportation systems, traffic signal control (TSC) in urban traffic networks has always been an important challenge. In recent years, model predictive control (MPC) has shown extraordinary potential in TSC due to its outstanding ability to model dynamic systems. However, the relatively complex online computing, lack of reasonable setpoints for target solving, and uncertainty of traffic network hinder MPC from being further applied. To address these problems, we propose a hierarchical, distributed, and robust model predictive control (HD-RMPC) framework for urban TSC. At the slow-update layer, the road network is dynamically divided into several subareas according to regional attributes and real-time traffic demand. Meanwhile, the volume is coordinated in a robust way for the purpose of traffic equilibrium and overflow prevention. Then, the set-point matrix of each subarea is calculated to equalize the flow in the subarea. This distributed framework guarantees the real-time performance of MPC in urban traffic networks. At the fast-update layer, we adopt an improved prediction model by explicit modeling of the disturbance and reduce the prediction error. Finally, the objective function is reconstructed and solved at the control layer to obtain the optimal control law. Through continuous and asynchronous optimization of the set point and prediction model, the framework significantly improves the control effect. Simulation evaluation based on a real-world road network demonstrates that the proposed HD-RMPC method outperforms all baselines and maintains excellent real-time performance. [ABSTRACT FROM AUTHOR]

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model's parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant. [ABSTRACT FROM AUTHOR]

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents. [ABSTRACT FROM AUTHOR]

Urban traffic control systems (UTCSs) are deployed to a great number of urban cities despite lacking feedback when adjusting the traffic signals. The development of reinforcement learning (RL) makes it possible to apply feedback to UTCS, and great efforts have been made on RL-based traffic control strategies. However, those studies are regardless of the traffic flow theory of the network and the road users' perspectives on the performance of traffic. This study proposes a multiagent reinforcement learning (MARL) based traffic control strategy, in which each intersection in a macroscopic fundamental diagram (MFD) region was controlled by one agent using the level of services (LOS) and MFD-based parameters as rewards. The proposed MARL strategy was evaluated by simulation in a 3×3 grid network compared with pretimed, actuated, and MFD-based traffic control strategies. The evaluation results showed that, at different demand levels, the proposed MARL strategy outperforms the other three traffic control strategies in terms of average intersection queue length and average intersection waiting time to a different extent. Results also showed that the proposed MARL dissipated the congestion faster than the other three control strategies. Results of the Friedman test indicated that the differences in performances between the proposed MARL and other strategies were statistically significant regardless of the demand level. The MFD in the testbed network controlled by the proposed MARL was different from that controlled by the pretimed strategy, especially the MFD scatter plot. It provides insights on considering the traffic flow theory of the network when applying MARL to traffic control strategies. [ABSTRACT FROM AUTHOR]

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a Leven berg Marquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R² ) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists. [ABSTRACT FROM AUTHOR]

Real-time traffic signal control has long been a critical way to improve traffic congestion. Transit Signal Priority (TSP) is seen as a cost-effective way to reduce travel time variability. Most of the previous studies develop real-time signal control systems on a vehicle basis, which is unable to efficiently provide preferential treatment on transit vehicles. Person-based signal control systems, which transform traffic delay computation units from vehicle to passenger, have been proposed to try to address this limitation. However, their models, optimizing signal plan cycle-by-cycle, cannot rapidly respond to traffic variations. This study proposes a Person-based Adaptive traffic signal control method with Cooperative Transit signal priority (PACT). In PACT, not only do Road-Side Units (RSUs) perform signal optimization, but also On-Board Units (OBUs) provide in-vehicle speed advisory to reduce delays. The interaction between RSU and OBU is conducted second-by-second, which has high adaptability to traffic variations. Experiments are performed based on real traffic data via traffic simulation platform SUMO. The results indicate that PACT can efficiently reduce delays of both bus passengers and auto passengers at a signalized intersection. Compared to preoptimized signal plans, the results show that each passenger on transit vehicles experiences 33%–70% decreases in delays, and each auto passenger experiences 3%–29% decreases in delays. PACT can reduce 80%–98% in delays when the occupancy weight factor is relatively large, showing the potential of extending PACT on performing signal preemption. [ABSTRACT FROM AUTHOR]

In recent years, urban traffic congestion has seriously affected the healthy development of urbanization in China. And many measures to combat congestion have had little effect. The purpose of this paper is to find out the most reasonable and sustainable measures to control traffic congestion. Based on the theory of system dynamics, this study constructs a model of the formation mechanism of urban traffic congestion in China, and analyzes the thinking error of the traditional strategy of "building roads to eliminate traffic congestion" This study includes the current policy measures to control traffic congestion in the system dynamics model and analyzes the influence of each measure on the formation mechanism of urban traffic congestion. Then, it critiques the unsustainability of rigid policies, such as the vehicle number limit and the "similar road building to control traffic congestion" policies. This study reveals that of the five policies adopted by the government to alleviate traffic congestion, and come to the conclusion: the "sparse block collocation" policy is the most sustainable and fundamental congestion control measure. To achieve efficient traffic congestion control and support the healthy development of urbanization in China in the future, the government should increase the balance of infrastructure investment to improve the slow environment of public transport, adhere to public transport-oriented land development policies, raise the cost of motor vehicle travel, and promote urban traffic. [ABSTRACT FROM AUTHOR]

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions. [ABSTRACT FROM AUTHOR]

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions. [ABSTRACT FROM AUTHOR]

The behavior of changing lanes has a great impact on road traffic with heavy traffic. Traffic flow density is one of the important parameters that characterize the characteristics of traffic flow, and it will also be affected by the behavior of changing lanes, especially in the case of each lane. The penetration of autonomous vehicles can effectively reduce lane-changing behavior. Studying the relationship between traffic flow density and lane-changing behavior under different autonomous vehicle penetration rates is of great significance for describing the operation mechanism of mixed traffic flow and the control of mixed traffic. In this article, we use empirical, simulation, and data-driven methods to analyze the urban expressway of autonomous vehicles with penetration rates of 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation experiment was carried out on the road, and data related to density, the rate of changing into the lanes, and the rate of changing out lanes were collected. The analysis of the experimental results found the following: (1) The increase in penetration of autonomous vehicles leads to a certain degree of downward trend in density, the rate of changing into the lanes, and the rate of changing out lanes. (2) Different lanes have different effects on the penetration of autonomous vehicles. In a 4-lane road, the two lanes farther from the entrance and exit are closer in appearance, while the two lanes closer to the entrance and exit are similar. (3) The relationship between density and the rate of changing into the lanes and the rate of changing out lanes shows a linear relationship with the penetration of autonomous vehicles. Although the performance of each lane is slightly different, in general, it can be carried out by a multiple regression model. The given parameter value range is relatively close under different permeability. In summary, autonomous vehicles effectively reduce the traffic density and lane-changing behavior of each lane. There is a linear relationship between traffic flow density and lane-changing behavior with the penetration of autonomous vehicles. The density-lane-changing behavior model proposed in this paper can better describe the relationship between the density of the circular multilane urban expressway and the lane-changing behavior in the case of a large traffic flow in mixed traffic. [ABSTRACT FROM AUTHOR]

Expressway, as the main artery of urban traffic, realizes the smooth operation of the whole urban road network through reasonably balancing the traffic flow. However, due to the lack of reasonable and effective traffic control, the safety and congestion of expressways are becoming more and more serious. The development of intelligent network technology provides a new idea to solve the control problem of expressways. In this paper, a data-driven ramp control model of urban expressway is constructed. The interaction of traffic information is realized through intelligent network connection technology. The cooperative control strategy of VSL and RM is adopted. The mutual feedback of VSL and RM is realized based on the improved METANET model. The simulation experiment based on VISSIM secondary development shows that the collaborative control strategy under the intelligent network environment could make the vehicle travel time reduced by 20.59% and the speed difference between adjacent sections of the expressway mainline by 34.07%, which realized the coordinated control of the mainline and the on-ramp under the intelligent network environment, alleviate the expressway traffic congestion, reduce the traffic pressure, and improve the efficiency of the road network. [ABSTRACT FROM AUTHOR]

In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of i -LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a linkbased methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions. [ABSTRACT FROM AUTHOR]

In recent years, urban traffic congestion has become more serious and the capacity of roads has declined, resulting in frequent traffic accidents. In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). Firstly, based on the traffic survey, the simulation model of the study area is built, and the basic data such as the traffic flow and the time occupation rate of each road section are obtained. Secondly, the simulation data are used to test the existence of MFD in the road network, and the controlled area is defined. Then, the vehicle change model of the road network area is established. Then, in view of the problem of poor adaptive ability of traditional PID control, the FR-PID control structure is designed. Finally, an example is verified by VISSIM software. In the simulation, different control methods are used for comparison and verification, and the simulation results are analyzed. The results show that the control effect of the proposed method is better than that of the traditional method, and the regional average accumulative vehicle number, regional average completed volume, regional accumulative delays, and total vehicle travel time are optimized by 28.21%, 41.19%, 27.06%, and 32.73%, respectively. The research results can provide reference for the management of urban congestion, thereby reducing the number of traffic accidents and improving urban traffic safety. [ABSTRACT FROM AUTHOR]

Fast road emergency response can minimize the losses caused by traffic accidents. However, emergency rescue on urban arterial roads is faced with the high probability of congestion caused by accidents, which makes the planning of rescue path complicated. This paper proposes a refined path planning method for emergency rescue vehicles on congested urban arterial roads during traffic accidents. Firstly, a rescue path planning environment for emergency vehicles on congested urban arterial roads based on the Markov decision process is established, which focuses on the architecture of arterial roads, taking the traffic efficiency and vehicle queue length into consideration of path planning; then, the prioritized experience replay deep Q-network (PERDQN) reinforcement learning algorithm is used for path planning under different traffic control schemes. The proposed method is tested on the section of East Youyi Road in Xi'an, Shaanxi Province, China. The results show that compared with the traditional shortest path method, the rescue route planned by PERDQN reduces the arrival time to the accident site by 67.1%, and the queue length at upstream of the accident point is shortened by 16.3%, which shows that the proposed method is capable to plan the rescue path for emergency vehicles in urban arterial roads with congestion, shorten the arrival time, and reduce the vehicle queue length caused by accidents. [ABSTRACT FROM AUTHOR]

Traffic signal optimization is a significant means for smoothing urban traffic flow. However, the operation of traffic signals is currently seriously constrained by the data available from traditional point detectors. In recent years, an emerging technology, connected vehicle (CV), which can percept the overall traffic environment in real time, has drawn researchers' attention. With the new data source, traffic controllers should be able to make smarter decisions. A lot of work has been done to develop a new traffic signal control pattern under connected-vehicle environment. This paper provides a comprehensive review of these studies, aiming at sketching out the state of the arts in this research field. Several basic control problems, communication, control input, and objectives, are briefly introduced. The commonly used optimization models for this problem are summarized into three types: rule-based models, mathematical programming-based models, and artificial intelligence-based models. Then some major technical issues are discussed in detail. Finally, we raise the limitation of the existing studies and give our perspectives of the future research directions. [ABSTRACT FROM AUTHOR]

Aimed at the problem of occasional congestion control, the cusp catastrophe theory is used to establish the catastrophe model of the urban road system under occasional congestion, finding breakpoints and analyzing stability after urban road system catastrophes by constructing the energetic function; based on the catastrophe characteristics of the urban road system, the feasibility method of congestion control is discussed. The results show that the control method of traffic flow based on catastrophe characteristics of the urban road system can effectively improve the efficiency of the road system in theory. Finally, the applicability of the control method based on catastrophe characteristics is analyzed by examples under different occasional congestion situations. [ABSTRACT FROM AUTHOR]

The adaptive traffic signal control (ATSC) problem can be modeled as a multiagent cooperative game among urban intersections, where intersections cooperate to counter the city's traffic conditions. Recently, reinforcement learning (RL) has achieved marked successes in managing sequential decision making problems, which motivates us to apply RL in the ATSC problem. One of the largest challenges of this problem is that the observation of intersection is typically partially observable, which limits the learning performance of RL algorithms. Considering the large scale of intersections in an urban traffic environment, we use independent RL to solve ATSC problem in this study. We model ATSC problem as a partially observable weak cooperative traffic model (PO-WCTM). Different from a traditional IRL task that averages the returns of all agents in fully cooperative games, the learning goal of each intersection in PO-WCTM is to reduce the cooperative difficulty of learning, which is also consistent with the traffic environment hypothesis. To achieve the optimal cooperative strategy of PO-WCTM, we propose an IRL algorithm called Cooperative Important Lenient Double DQN (CIL-DDQN), which extends Double DQN (DDQN) algorithm using two mechanisms: the forgetful experience mechanism and the lenient weight training mechanism. The former mechanism decreases the importance of experiences stored in the experience reply buffers, while the latter mechanism increases the weight experiences with high estimation and ‘leniently’ trains the DDQN neural network. Experiments in two real traffic scenarios and one simulated traffic scenarios show that, CIL-DDQN outperforms other methods in almost all performance indicators of ATSC. [ABSTRACT FROM AUTHOR]

Urban Traffic Control is a key problem for most big cities. Current approaches to handle the city traffic rely on controlling traffic lights. The systems in operation range from static control of traffic light phases to adaptive systems based on numeric models and traffic sensors. Recently, some planning‐based approaches have also been proposed. These approaches work at a higher level of abstraction, but have been found to work well if complemented by low‐level systems. We have identified two main difficulties for the wide use of planning techniques in this domain: generating the control models is a difficult task; and some algorithms scale poorly. In this paper we present Automated Planning for Traffic Control (APTC), a control system based on Automated Planning, that successfully overcomes these two problems. It combines techniques that continuously: learn an accurate planning model; and also divide the city for distributed reasoning in order to scale to large city networks. Experimental results show that APTC outperforms static approaches as well as other planning‐based systems. We also show that the combination of both approaches improves compared with using only one of them. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) face the formidable challenge of large-scale missing data, particularly in the imputation of traffic data. Existing studies have mainly relied on modeling network-level spatiotemporal correlations to address this issue. However, these methods often overlook the rich semantic information (e.g., road infrastructure, sensor location, etc.) inherent in road networks when capturing network-wide spatiotemporal correlations. We address this limitation by presenting the Graph Transformer-based Traffic Data Imputation (GT-TDI) model, which imputes missing values in extensive traffic data by leveraging spatiotemporal semantic understanding of road networks. The proposed model leverages semantic descriptions that capture the spatial and temporal dynamics of traffic across road networks, enhancing its capacity to infer comprehensive spatiotemporal relationships. Moreover, to augment the model's capabilities, we employ a Large Language Model (LLM) and prompt engineering to enable natural and intuitive interactions with the traffic data imputation system, allowing users to query and request in plain language, without requiring expert knowledge or complex mathematical models. The proposed model, GT-TDI, utilizes Graph Neural Networks (GNN) and Transformer architectures to perform large-scale traffic data imputation using deficient observations, sensor social connectivity, and semantic descriptions as inputs. We evaluate the GT-TDI model on the PeMS freeway dataset and benchmark it against cutting-edge models. The experimental evidence demonstrates that GT-TDI surpasses the cutting-edge approaches in scenarios with intricate patterns and varying rates of missing data. • A novel semantic description for traffic data imputation that captures spatiotemporal correlations flexibly. • A pioneering application of prompt engineering to ITS that bridges the gap between LLMs and ITS. • The proposed model leverages LLMs and semantic prompts for traffic data imputation. • Extensive experiments on real-world traffic data show the superiority. • A potential framework for integrating LLMs and semantic prompts into other ITS tasks. [ABSTRACT FROM AUTHOR]