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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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

Recently, with increasing urban traffic congestion, there has been an upsurge in studies on reinforcement learning for traffic signal control (RL-TSC), which enables efficient traffic management. However, most existing RL-TSC research relies on simulation, and instances of real-world application are relatively scarce. Furthermore, existing RL-TSC methods employ a step-based approach, controlling traffic signals every short step. This approach can be inefficient in oversaturated traffic conditions, where large deviations in traffic demand between movements prevent traffic signals from being effectively managed based on the overall traffic situation. In this study, we aim to transform simulation-based RL-TSC into a more practical and applicable model. We have developed a RL-TSC method capable of responding to oversaturated traffic conditions, designing an action space that enables the agent to derive an optimal signal set for every cycle length, thereby understanding the traffic situation across all movements. During each cycle length, our proposed model conducts traffic signal optimization and identifies the optimal signal through an iterative exploration. To facilitate a fast and accurate strategy search process, we developed a kinematic wave-based mesoscopic model. This model estimates the density across the entire link, based on collected traffic data and derives the state and reward values. We have validated the field applicability of the proposed RL-TSC method through a real-world demonstration at a congested intersection in Seoul, Korea. The results indicated a significant improvement in traffic congestion, with the average queue length at the intersection reduced by up to 11.4%. [ABSTRACT FROM AUTHOR]

Due to increasing traffic demand, many metropolitan areas are experiencing extensive traffic congestion, which demands for efficient traffic signal timing and optimization. However, conventional efficiency measure-based signal optimization cannot handle the ubiquitous uncertainty in the road networks, demanding for the incorporation of reliability measures into signal optimization, which is still in its early stage. Therefore, targeting this issue, based on the recent studies on recognizing travel time reliability (TRR) as an important reliability measure of road networks, a travel time reliability-based urban road traffic network signal timing optimization model is proposed in this paper, with the objective function to optimize a TTR measure, i.e., buffer time index. The proposed optimization model is solved using the heuristic particle swarm optimization approach. A case study is conducted using microscopic traffic simulation for a road network in the City of Nanjing, China. Results demonstrate that the proposed optimization model can improve travel time reliability of the road traffic network and the efficiency of the road traffic network as well. Future studies are recommended to expand the integration of travel time reliability into traffic signal timing optimization. [ABSTRACT FROM AUTHOR]

One of the major problems of both developed and developing countries is traffic congestion in urban road transportation systems.Some of the adverse consequences of traffic congestion are loss of productive time,delay in transportation,increase in transportation cost,excess fuel consumption,safety of people,increase in air pollution level and disruption of day-to-day activities.Researches have shown that among others,traditional traffic control system is one of the main reasons for traffic congestion at traffic junctions.Most countries through out the world use pre-timed/ fixed cycle time traffic control systems.But these traffic control systems do not give an optimal signal time setting as they do not take into account the time dependent heavy traffic conditions at the junctions.They merely use a predetermined sequence or order for both signal phase change and time setting.Some times this also leads to more congestion at the junctions.As an improvement of fixed time traffic control method, fuzzy logic traffic control model was developed which takes into account the current traffic conditions at the junctions and works based on fuzzy logic principle under imprecise and uncertain conditions.But as a real life situation,in addition to uncertainty and impreciseness there is also indeterminacy in traffic signal control constraints which fuzzy logic can not handle.The aim of this research is to develop a new traffic signal control model that can solve the limitations of fixed time signal control and fuzzy logic signal control using a flexible approach based on interval-valued neutrosophic soft set and its decision making technique,specially developed for this purpose.We have developed an algorithm for controlling both phase change and green time extension / termination as warranted by the traffic conditions prevailing at any time.This algorithm takes into account the existing traffic conditions,its uncertainty and indeterminacy.The decision making technique developed allows both phase change and green time setting to be managed dynamically,depending on the current traffic intensity and queuing of vehicles at different lanes,as opposed to an order or a pre-determined sequence followed in existing traffic control models. [ABSTRACT FROM AUTHOR]

• Fusion process between signals shows that traffic flow undergoes phase transition. • Traffic network is modelled as an Ising model with traffic flow taking on two states. • Transition from congestion becomes more apparent when control undergoes Parrondo switching. While increasing urban traffic can be an indicator of development, this inevitably results in traffic congestion in urban road networks. Is there a way to manage traffic flow through the control of traffic signals such that the overall network congestion is improved? Traffic light signals can be represented as two states of an Ising model. It is possible for traffic lights to "communicate" with each other through a fusion process from a remote management control system. This requires collection of information which can be fed to a centralized decision-making control mechanism. We first explore the fusion process between traffic signals and show that it is possible for traffic flow in a city to follow the phase transition as exhibited in the 2D Ising model. The model will be extended to show that a random switching between signalling control mechanisms can result in congested traffic being susceptible to transit out of congestion. [ABSTRACT FROM AUTHOR]

This paper introduces two traffic light strategies to control traffic and avoid traffic jam in urban networks. One strategy is a new traffic light scheduling system, which controls traffic light using local variables (waiting time and number of vehicle on links) but has a global impact on the traffic, using shared variables between neighbour intersections. The proposed traffic light scheduling system is designed based on eigenvector centrality of intersection relation matrix. The intersection relation matrix is a new representation of a junction which indicates the traffic relation between intersection's links and adjacent intersections. The second contribution is expanding a new dual mode traffic light strategy (namely, Exit Status Traffic Light (ETL)), which notifies the drivers whether they are allowed to exit a street or not. In other words, vehicles are allowed to enter a street in both red and green ETL, but they are not allowed to exit the street for a long time in red ETL (while traffic is heavy in the subnetwork). The ETL gives a chance to relax traffic in a subnetwork and avoid traffic jam. The effectiveness of the proposed strategy is analysed and evaluated by a number of simulations on three-way grid networks. Two-way rectangular grid networks are modelled via a cell transmission model (CTM). The macroscopic fundamental diagram (MFD) and the number of jammed cells are compared with two state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$ -learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion. [ABSTRACT FROM AUTHOR]

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network. [ABSTRACT FROM AUTHOR]

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively. [ABSTRACT FROM AUTHOR]

Traffic signal optimisation may lead to the alleviation, to some extent, of urban traffic congestion, particularly by using real-time data rather than expected traffic flow data. Recent advances in radar technology have made it possible to observe detailed traffic flow data in and around roadway intersections in real time. The notion of self-organisation has relatively recently been proposed as a promising alternative to improve the effective allocation of green time, particularly under lighter traffic conditions. A fixed-time control strategy and seven self-organising algorithms are compared in a microscopic traffic simulation model of a provincial road in the Western Cape province of South Africa. Actual arrival rates are used as input for the model, while the algorithms are compared using six performance measure indicators, under both light and moderate traffic conditions. The results are used to make a case for the adoption of self-organising traffic signal control algorithms, especially under conditions of light to moderate traffic densities, since this can lead to significant improvements in traffic flow in terms of delay time, vehicle stops, and time spent travelling at unacceptably slow speeds through the road network. [ABSTRACT FROM AUTHOR]

The concept of resilience can be realized in natural and engineering systems, representing the ability of a system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation systems, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations are still missing. Here, we define city traffic resilience based on the spatiotemporal clusters of congestion in real traffic and find that the resilience follows a scale-free distribution in 2D city road networks and 1D highways with different exponents but similar exponents on different days and in different cities. The traffic resilience is also revealed to have a scaling relation between the cluster size of the spatiotemporal jam and its recovery duration independent of microscopic details. Our findings of universal traffic resilience can provide an indication toward better understanding and designing of these complex engineering systems under internal and external disturbances. [ABSTRACT FROM AUTHOR]

Traffic congestion in urban networks may lead to strong degradation in the utilization of the network infrastructure, which can be mitigated via suitable control strategies. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that controls the traffic light parameters in an urban network to reduce traffic congestion. A nearly optimal control formulation is adopted to avoid the curse of dimensionality occurring in the solution of the corresponding Hamilton–Jacobi–Bellman (HJB) optimal control problem. First, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov function; then, the solution is updated at each iteration in such a way to approach the nearly optimal solution, using a close-to-optimality index and information coming from the simulation model of the network (simulation-based design). Simulation results obtained using a traffic simulation model of the network Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach, as compared with alternative traffic strategies based on a simplified linear model of the traffic network. It is shown that the proposed strategy can adapt to different traffic conditions and that low-complexity parametrizations of the optimal solution, a linear and a bimodal piecewise linear strategy, respectively, provide a satisfactory trade-off between computational complexity and network performance. [ABSTRACT FROM AUTHOR]

The majority of fuel consumed in traffic on urban arterials is related to driving in congested traffic, characterized by frequent speed fluctuations and stops at signalized intersections. A range of traffic control strategies was suggested in the past to decrease fuel consumption and emissions in urban networks. The research presented in this study aims to fill the gap in the existing knowledge by proposing a novel combination of an ecology-based performance index and an evolutionary method for optimization of traffic signal settings. An optimization problem is defined to find the best values for traffic light control parameters on the street network. The ecology-based performance index is used as the criteria for optimization. The defined problem is solved by the Bee Colony Optimization (BCO) technique. The geometry of the subject network has been inserted into a Vissim model calibrated by using relevant traffic demand and field-measured travel times. The Vissim has been used as the tool for the evaluation of the proposed BCO, TRANSYT-7F (T7F) and field signal timing plans. The methodology is tested on the field-real-like network of the City of Kragujevac, Serbia. Results show that in the case of the overall network ecology performance index, BCO makes a decrease of 11.95 % and 8.47% compared to the signal timings from field and T7F, respectively. • Proposed an ecology-based fixed-time control logic for street networks. • The model uses a BCO algorithm and derives control parameters. • A VISSIM simulation estimates the quality of the proposed control algorithm. • TRANSYT-7F and field signal plans are used for comparison with the BCO algorithm. • The model is tested on the field-real-like network of the City of Kragujevac. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of intelligent transportation system around the world, which helps to relieve urban traffic congestion problems. For instance, many mega-cities in China have devoted a large amount of money and resources to the development of intelligent transportation system. This poses an intriguing and important issue: how to measure and quantify the contribution of intelligent transportation system to the urban city, which is still a puzzle. This paper proposes a matching difference-in-difference model to calculate the contribution rate of intelligent transportation system on traffic smoothness. Within the model, the main effect indicators of traffic smoothness are first identified, and then the evaluation index system is built, and finally the ideas of the matching pool are introduced. The proposed model is illustrated in Guangzhou, China (capital city of Guangdong province). The results show that introduction of ITS contributes 9.25% to the improvement of traffic smooth in Guangzhou. Also, the research explains the working mechanism of how ITS improves urban traffic smooth. Eventually, some strategy recommendations are put forward to improve urban traffic smooth. [ABSTRACT FROM AUTHOR]

Mitigating traffic congestion on urban roads, with paramount importance in urban development and reduction of energy consumption and air pollution, depends on our ability to foresee road usage and traffic conditions pertaining to the collective behavior of drivers, raising a significant question: to what degree is road traffic predictable in urban areas? Here we rely on the precise records of daily vehicle mobility based on GPS positioning device installed in taxis to uncover the potential daily predictability of urban traffic patterns. Using the mapping from the degree of congestion on roads into a time series of symbols and measuring its entropy, we find a relatively high daily predictability of traffic conditions despite the absence of any priori knowledge of drivers' origins and destinations and quite different travel patterns between weekdays and weekends. Moreover, we find a counterintuitive dependence of the predictability on travel speed: the road segment associated with intermediate average travel speed is most difficult to be predicted. We also explore the possibility of recovering the traffic condition of an inaccessible segment from its adjacent segments with respect to limited observability. The highly predictable traffic patterns in spite of the heterogeneity of drivers' behaviors and the variability of their origins and destinations enables development of accurate predictive models for eventually devising practical strategies to mitigate urban road congestion. [ABSTRACT FROM AUTHOR]

Short distance trips are defined as any trips shorter than or equal to 5 kilometers, which have been found to be a big contributor to the traffic congestion problem. This paper is intended to analyze factors that influence the mode choice of short distance travels in order to help reduce short distance trips by cars. A survey is conducted at two typical kinds of residential areas, one with a high proportion of short distance car trips and another one with a low proportion. Then, by applying the structural equation modeling, it is found that the age, the household income, and the vehicle ownerships have a significant effect on the mode choice of short distance travels. Besides, among residents of the same type (same age, household income, and vehicle ownerships) in surveyed areas, those in the area with a better green-mode travel environment account for a higher proportion choosing the green mode than those in other areas. Based on this result, it is concluded that a better green-mode travel environment leads to a higher proportion of green-mode travels. In the end, the paper shows residents’ stated willingness to change travel modes from cars to the green mode. [ABSTRACT FROM AUTHOR]

Region-level traffic information can characterize dynamic changes of urban traffic at the macro level. Real-time region-level traffic prediction help city traffic managers with traffic demand analysis, traffic congestion control, and other activities, and it has become a research hotspot. As more vehicles are equipped with GPS devices, remote sensing data can be collected and used to conduct data-driven region-level-based traffic prediction. However, due to dynamism and randomness of urban traffic and the complexity of urban road networks, the study of such issues faces many challenges. This paper proposes a new deep learning model named TmS-GCN to predict region-level traffic information, which is composed of Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU). The GCN part captures spatial dependence among regions, while the GRU part captures the dynamic change of traffic within the region. Model verification and comparison are carried out using real taxi GPS data from Shenzhen. The experimental results show that the proposed model outperforms both the classic time series prediction model and the deep learning model at different scales. [ABSTRACT FROM AUTHOR]

Due to the limitations of data transfer technologies, existing studies on urban traffic control mainly focused on isolated dimension control such as traffic signal control or vehicle route guidance to alleviate traffic congestion. However, in real traffic, the distribution of traffic flow is the result of multiple dimensions whose future state is influenced by each dimension's decisions. Presently, the development of the Internet of Vehicles enables an integrated intelligent transportation system. This paper proposes an integrated intelligent transportation model that can optimize predictive traffic signal control and predictive vehicle route guidance simultaneously to alleviate traffic congestion based on their feedback regulation relationship. The challenges of this model lie in that the formulation of the nonlinear feedback relationship between various dimensions is hard to describe and the design of a corresponding solving algorithm that can obtain Pareto optimality for multi-dimension control is complex. In the integrated model, we introduce two medium variables—predictive traffic flow and the predictive waiting time—to two-way link the traffic signal control and vehicle route guidance. Inspired by game theory, an asymmetric information exchange framework-based updating distributed algorithm is designed to solve the integrated model. Finally, an experimental study in two typical traffic scenarios shows that more than 73.33% of the considered cases adopting the integrated model achieve Pareto optimality. [ABSTRACT FROM AUTHOR]

Operators of parking guidance and information systems PGIS often encounter difficulty in determining when and how to provide reliable car park availability information to drivers. Reliability has become a key factor to ensure the benefits of urban PGIS. The present paper is the first to define the guiding parking reliability of urban parking variable message signs (VMSs). By analyzing the parking choice under guiding and optional parking lots, a guiding parking reliability model was constructed. A mathematical program was formulated to determine the guiding parking reliability of VMS. The procedures were applied to a numerical example, and the factors that affect guiding reliability were analyzed. The quantitative changes of the parking berths and the display conditions of VMS were found to be the most important factors influencing guiding reliability. The parking guiding VMS achieved the best benefit when the parking supply was close to or was less than the demand. The combination of a guiding parking reliability model and parking choice behavior offers potential for PGIS operators to reduce traffic congestion in central city areas. [ABSTRACT FROM AUTHOR]

Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [ABSTRACT FROM AUTHOR]

A space-time network is developed that represents traffic flows over time for a capacitated road transportation system having one-way and two-way streets. Traffic signal lights are explicitly incorporated into the network structure so that total travel time is a piecewise linear convex function of the number of units traveling on the streets. Hence congestion effects are explicitly considered while maintaining the linear nature of the model. The first example presented has one source and one sink. There is a unimodal buildup of traffic at the source (say a factory) which enters the street network as quickly as its capacity permits and proceeds through the network, stopping at red lights when necessary, toward the sink (a residential area). Two efficient solution methods are used: a network flow solution suitable for a multiple-source single-destination network, and a shortest path solution suitable only for a single-source single-destination network. Computations show that the arrival rate has multiple peaks which are induced by the stop lights. The second example has multiple sources and one sink and gives similar results, except that the arrival rate has a single board peak which is due to the extreme symmetry of the constraints of the problem. [ABSTRACT FROM AUTHOR]

A series of vehicular traffic experiments conducted in Austin, Texas, shows the reasonableness of the two assumptions in the two-fluid (moving and stopped vehicles) model of town traffic. The observational data support the assumption that the average speed in an urban street network is proportional to the fraction of the vehicles moving raised to a power and is also in agreement with the supposition that during relatively uniform periods the traffic is ergodic. An important consequence is that the average of the fraction of time stopped for a test vehicle circulating in a street network is approximately equal to the average fraction of the vehicles stopped in the system during the same test period. The parameters of the two-fluid model and the observed ranges of trip time and stop time per unit distance have been shown to be effective in assessing or rank ordering the relative quality of traffic service in a number of Texas cities; comparisons are made with various cities around the world. The two-fluid methodology appears to be useful in a preliminary "before"/after" study during which signal timing changes were made. Finally, a preliminary analysis of aerial photographic data in two cities allows the determination of an additional two-fluid model parameter, p, in the relation stating that the fraction of vehicles stopped is given by the ratio of concentration to the jam or maximum concentration raised to a power, p. It is suggested that this parameter may be useful in describing the relative quality of various traffic systems. [ABSTRACT FROM AUTHOR]

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion. [ABSTRACT FROM AUTHOR]

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

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method. [ABSTRACT FROM AUTHOR]

Frustrations, monetary losses, lost time, high fuel consumption and CO 2 emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS–Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO 2 emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%). [ABSTRACT FROM AUTHOR]