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

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

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

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

In recent years, the problem of traffic congestion has become increasingly serious, and research on traffic system control has become a new hotspot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable support points can alleviate traffic congestion from a new perspective. This article improves the full speed differential model considering strong wind models from the perspective of bifurcation control to adjust traffic flow. This article theoretically proves the existence conditions of Hopf bifurcation and saddle node bifurcation in the model and finds the stability mutation point of the transportation system stability. A nonlinear system feedback controller was designed for unstable bifurcation points using Chebyshev polynomial approximation and random feedback control methods. Without changing the system equilibrium point, the advance, delay, and elimination of Hopf bifurcation were achieved, and the abrupt behavior of the transportation system was controlled, thereby alleviating traffic congestion. This article explains the changes in the stability of complex transportation systems from the perspective of bifurcation analysis, which can better capture the characteristics of traffic flow. By adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the transportation system is fully described, and the influence of unstable focal points and saddle points on the system is suppressed, thereby slowing down the traffic flow. In addition, unstable bifurcation points can be eliminated, and the Hopf bifurcation can be controlled to advance, delay, and disappear, thereby achieving control over the stable behavior of the transportation system. This helps alleviate traffic congestion and also helps describe actual traffic phenomena. [ABSTRACT FROM AUTHOR]

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

Information about spatial distribution (OD flows) is a key element in traffic management systems in urban transport networks that enables efficient traffic control and decisions to redirect traffic to less congested sections of the network in emergencies. With the development of modern techniques of remote sensing, more and more advanced methods are used to measure traffic and determine OD flows. However, they may produce results with different levels of errors caused by various factors. The article examines the impact of traffic volume and its variability on the error values of short-term prediction of the OD matrix in the urban network. The OD flows were determined using a deep learning network based on data obtained from video remote sensing devices. These data were recorded at earlier intervals concerning the forecasting time. The extent to which there is a correlation between the size of OD flows and the prediction error was examined. The most frequently used measure of prediction accuracy, i.e., MAPE (mean absolute percentage error), was considered. The analysis carried out made it possible to determine the ranges of traffic flow rate for which the MAPE stabilizes at the level of approximately 6%. A set of video remote sensing devices was used to collect spatiotemporal data. They were located at the entrances and exits from the study area on important roads of a medium-sized city in Poland. The conclusions obtained may be helpful in further research on improving methods to determine OD matrices and estimate their reliability. This, in turn, involves the development of more precise methods that allow for reliable traffic forecasting and improve the efficiency of traffic management in urban areas. [ABSTRACT FROM AUTHOR]

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

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]

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

• The study investigated the psychological experiences of regular commuters in traffic congestion using a three-phase sequential exploratory mixed design. • Phase 1 established a thematic network of travel adversities, psychological experiences, and consequences with 57 fundamental themes categorized and assigned under the organizing themes of travel adversity, negative affect, fight, flight, and negative road occurrences. • In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). • Phase 3 statistically validated the model of the vicious cycle, based on which a mathematical model was formulated for further interdisciplinary inquiries on the prediction and control of traffic behavior in real-world settings. This study investigated the psychological experiences and consequences of travel adversities during traffic congestion using a three-phase sequential exploratory mixed-methods design. Phase 1 explored the travel adversities, psychological experiences, and consequences of a sample of ten (four women and six men) regular commuters of Bangalore's congested roads using semi-structured interviews. In phase 2, a checklist was developed listing the fundamental themes from phase 1 with Likert-type responses ranging from 0 (never) to 5 (always). Phase 3 gathered data in the checklist and tested the statistical validity of the thematic model in a sample of 190 (81 women and 103 men) regular commuters. Attride-Stirling model thematic network was established with 57 fundamental themes categorized and assigned under the organizing themes of travel adversities (n = 6), negative affect (n = 28), fight (n = 7), flight (n = 6), and negative road occurrences (n = 10), in the global theme, psychological experiences and consequences. Structural equation modeling indicated that (1) negative affect significantly predicted fight and flight, (2) fight is a significant predictor of negative road occurrences, and (3) psychological experiences and consequences create a self-perpetuating cycle, with travel adversity triggering negative emotions, which results in fight responses leading to negative road occurrences, further intensifying travel adversity. A mathematical model is established based on this statistical validation, which holds potential applications in real-time traffic algorithms. [ABSTRACT FROM AUTHOR]

Identification and evaluation of traffic congestion are critical for better management of urban traffic, especially at merge sections where two different traffic streams converge and generate shockwaves. This study evaluates the travel time reliability measure (travel time index (TTI)) as a reliable congestion measure by analysing the impact of traffic flow parameters such as traffic volume, capacity, road geometrics and conflicting traffic flows at merge sections on TTI under mixed traffic conditions. The data required for congestion analysis were collected through video recording. Statistical models were developed to: (a) evaluate the relationship between road geometric characteristics and capacity of merge sections; (b) estimate traffic density and its evolution with time and compare it against occupancy values; and (c) correlate the relationship between TTI and traffic behaviour at merge sections and determine the variations in TTI for different levels of service of merge sections. The models developed are statistically validated using secondary data sets from the same study section, as well as from data collected from other multi-lane merge sections. The developed model successfully estimated recurring congestion by considering both space and time. This study highlights the potential usage of TTIs in capturing travel time variations more accurately at merge sections under mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Summary: Road accidents and traffic congestion are the unavoidable consequences of the growing number of vehicles on the road. Conventional management systems are not effective in mitigating these issues. However, a range of technologies, including sensors, processors, vehicle‐based resources, and mobile cloud computing, can help solve traffic and safety problems. Vehicular cloud networks, a cutting‐edge technology for the development of intelligent transportation systems, face significant challenges, such as ensuring reliable service providers that maintain stable connections between vehicles. Roadside units (RSUs) play a crucial role in these networks, but they come with several drawbacks, including high costs, imbalanced load distribution, and unreliable connections. To address these issues, we propose the Traffic Control and Safety in Vehicular Networks (TCaS‐VN) algorithm, a cloud‐based sharing services framework for vehicles that eliminates the need for RSUs. This approach offers several advantages, including a twofold improvement in the lifetime of clusters, the elimination of noncluster head nodes, a 54% reduction in overhead, a 58% reduction in service search time, and a success rate of over 95% in service provisioning. [ABSTRACT FROM AUTHOR]

The research of traffic congestion control has been developed rapidly in recent years. Researching the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation characteristics and nonlinear control of traffic flow based on the continuum model considering the new energy vehicles effect. First, a macroscopic traffic flow model considering the number of new energy vehicles and their range is proposed. Then, we transform it into a traffic flow stability model suitable for branching analysis using the traveling wave transformation. Therefore, we can transform the traffic flow problem into the stability analysis problem of systems and reflect unstable traffic characteristics such as congestion. Based on the stability model, the existence conditions of the Hopf bifurcation are proved and some bifurcation points of the traffic system are found. Second, we study the congestion and stability abrupt change behaviors near the equilibrium point and branching point to seek for the formation mechanism of traffic congestion. Finally, for the unstable bifurcation point, the control scheme is designed by Chebyshev polynomial approximation, stochastic feedback control, and other methods to delay or disappear the unstable bifurcation point and alleviate traffic congestion. It has important theoretical significance and application value. [ABSTRACT FROM AUTHOR]

Congestion in large and growing cities is a significant issue that harms the economy, travelers, and the ecosystem. Forecasting the degree of congestion on a road network in advance can help to avoid it and boost the network's capacity and performance. Despite its importance, however, traffic congestion prediction is not a popular topic among researchers and traffic engineers. It's because there aren't enough effective computational traffic forecast techniques or high-quality citywide traffic data. In the existing works, there are several Artificial Intelligence (AI) models are developed for the prediction of traffic congestion in the transport networks. Yet, it facing the problems and challenges relevant to the following parameters: ineffective data handling, prediction error, high time for training and testing operations. Therefore, the proposed work aims to implement a novel AI model for accurately forecasting the traffic congestion in the transportation networks. Here, the Cascaded Transition Recurrent Feature Network (CTRFN) technique is used to extract the best and useful features related to congestion from the traffic dataset. After that, the Paramount Transfer Learning Network (PTLN) model is deployed to predict the traffic congestion based on the extracted features. During this process, the Coherent Lizard Search Optimization (CLSO) algorithm is used to compute the weight factor for constructing the covariance matrix, which supports to obtain the reduced prediction error outputs. Moreover, the congestion prediction performance of the proposed model is validated and compared by using different measures. [ABSTRACT FROM AUTHOR]

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations. • Using Gaussian process (GP) for Traffic State Estimation (TSE). • Capturing congestion propagation in traffic wave with a rotation kernel. • Leveraging inter-lane correlation to improve TSE accuracy. • Model works well on small datasets and can quantify the TSE uncertainty. • Experiments on real-world and simulated data show GP-TSE accuracy and robustness. [ABSTRACT FROM AUTHOR]

• Simulation analysis of morning and evening peak traffic in underground loop reveals congestion patterns. • Developed control strategies: ramp control, plot control, and fast entry without railing. • Evaluation and verification of control schemes using simulation technology. • Findings provide reference for congestion control measures, improving capacity and ensuring safe operations. • Effective traffic control strategies for network-type underground loops in CBD areas. In recent years, there has been a growing trend of developing and constructing network-type underground loops in urban central business districts (CBDs) to complement interconnected underground spaces. However, the high volume of arrival and departure traffic, the impact of traffic patterns, and the complexity of traffic organization in CBD areas pose challenges to the efficient operation of underground loops. Accurate identification of congestion patterns and effective traffic control strategies are crucial to enhance capacity and ensure safe operations of underground loop systems. This paper presents a simulation analysis of morning and evening peak traffic scenarios in a designed underground loop to identify congestion patterns and their underlying causes. Based on the analysis, a combination of traffic control strategies, including ramp control, plot control, and fast entry without railing, is proposed and evaluated through simulations. The findings of this study serve as a reference for developing traffic congestion control measures in underground loop systems, contributing to improved capacity and safe operations. [ABSTRACT FROM AUTHOR]

• Develop a novel two-layer coordinated multi-agent reinforcement learning algorithm. • Synergize traffic signals through local cooperation and global coordination. • Optimization of vehicle emissions and traffic congestion as simultaneous goals. • Proposed method is performed in synthetic network and Cologne urban network on SUMO. Intersection traffic signal control considering vehicle emissions has become an important topic, however, the decision complexity of traffic signal control increases dramatically in a dynamic traffic environment with multi-intersections. It is a severe challenge to coordinate traffic signals at multi-intersections based on Internet of Things information to improve the traffic condition of the road network. This paper proposes a two-layer coordination algorithm based on multi-agent reinforcement learning—Multi-agent Coordinated Policy Optimization (MACoPO), for solving traffic signal control at multi-intersections. MACoPO consists of local cooperation, which adjusts the weights of individual rewards and neighborhood agents' rewards by using local cooperation factors (LCF), and global coordination, which updates the LCF to maximize global rewards. The state and reward functions are designed in terms of the current state of the signal, waiting queue length, vehicle density and emission concentration in the lane, vehicle delay, and vehicle emissions, thus making full use of the intersection state information. The proposed method is extensively assessed through simulation experiments using artificial and real road networks and the numerical results confirm its effectiveness in complex and dynamic real-time traffic environments with multi-intersections. [ABSTRACT FROM AUTHOR]