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This study emphasises the criticality of delay as a performance metric for signalized intersections and the challenges associated with its estimation, particularly in the context of Multi-class and Lane-free (MCLF) traffic conditions. Traditional delay models are often inadequate for such conditions, necessitating the development of a tailored approach. A novel delay equation is proposed, integrating insights from queuing theory principles with consideration of multi-class of vehicles and lane-free movement. Key features include assumption of random arrival and departure pattern as well as distribution, incorporation of Passenger Car Equivalent (PCE) and virtual lane concepts to account for the diverse vehicle classes and lane-free movement prevalent in Indian traffic. The model’s efficacy is demonstrated through comparison with conventional in practice delay models, showing its superior performance. This tailored approach enhances the accuracy of delay estimation and also highlights the importance of accounting for specific traffic characteristics in optimising signal design for intersections under MCLF traffic conditions.

Capturing congestion propagation among different facilities at intersections in dynamic stochastic traffic environments poses significant challenges, particularly under oversaturated conditions. In this paper, we present an [Formula: see text] feedback fluid queueing network model to address CPDSE, integrating random traffic demand, time-varying transition probabilities, and state-dependent stochastic service capabilities. A recursive algorithm is developed to analyze the feedback queueing network model. Simulation experiments reveal that the proposed model and algorithm perform effectively, irrespective of variations in traffic intensity. Compared to the mean results of 200 simulations, the average absolute error is 0.5152 vehicles, and the average relative error is 6.43% across three demand scenarios. Based on the proposed feedback queueing network model, two optimization frameworks are established for traffic signal control, aimed at minimizing either the average vehicle delay time or total costs, including fuel consumption. We propose a rolling optimization strategy that incorporates the mesh adaptive direct search algorithm to achieve real-time traffic signal control. Numerical experiments using actual survey data from Kunshan City yield several noteworthy findings: (1) An optimal moderate-sized time step exists for rolling optimization to minimize either the average delay time or total costs; specifically, an excessively small time step may increase vehicle average delay time or total costs; (2) The percentage of delay reduction achieved by our method, compared to Synchro software, reaches a maximum of approximately 70% when traffic demand is moderate and the initial state is low; and (3) The percentage reduction in average delay or total costs compared to Synchro initially increases and then decreases with rising traffic intensity.

In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.

Transformer-based models have demonstrated outstanding performance in trajectory prediction; however, their complex architecture demands substantial computing power, and their performance degrades significantly in long-term prediction. A transformer model was developed to predict vehicle trajectory in urban low-speed T-intersections. Microscopic traffic simulation data were generated to train the trajectory-prediction model; furthermore, validation data focusing on atypical scenarios were also produced. The appropriate loss function to improve prediction accuracy was explored, and the optimal input/output sequence length for efficient data management was examined. Various driving-characteristics data were employed to evaluate the model’s generalization performance. Consequently, the smooth L1 loss function showed outstanding performance. The optimal length for the input and output sequences was found to be 1 and 3 s, respectively, for trajectory prediction. Additionally, improving the model structure—rather than diversifying the training data—is necessary to enhance generalization performance in atypical driving situations. Finally, this study confirmed that the additional features such as vehicle position and speed variation extracted from the original trajectory data decreased the model accuracy by about 21%. These findings contribute to the development of applicable lightweight models in edge computing infrastructure to be installed at intersections, as well as the development of a trajectory prediction and accident analysis system for various scenarios.

In this paper, we propose a right-of-way optimization model considering multi-objective DEA evaluation for intersections in mixed driving environments with automated and human driving. Considering average speed, number of cars, penetration of automated vehicles, queuing pattern, left-turn rate, and number of buses as factors influencing intersection rights-of-way. Comprehensively consider the per capita delay, travel time and traffic volume as the optimization objectives, and then determine the weights of the three optimization objectives for each strand of traffic flow, and calculate the cross-benefit by interchanging the weight evaluation through the Crossing Efficiency Evaluation Method (CREE) to determine the optimal order of traffic flow in each direction at the intersection. In this paper, the optimization strategy is compared with existing benchmarks (e.g., actuated control) using SUMO simulation software, and the simulation results show that the proposed optimization strategy is able to shorten the per capita delay and travel time at intersections in order to improve the efficiency of the traffic flow compared to actuated control and the First-Come, First-Served strategy.

Aiming at the existing problems about the overflow prevention goal and the overall traffic efficiency guarantee being difficult to optimize at the same time in the signal control process of short-distance intersections scenario, this paper proposes a traffic signal active control method based on key state prediction. In order to construct the key state evolution trend of short-distance intersection scenarios, this paper proposes the concept of overflow index for short-distance road sections and designs the prediction method of overflow index. In order to perform fast computation and solution for the active control scheme, this paper builds a solution algorithm based on deep reinforcement learning and optimizes the problem of reward sparsity in the algorithm, which improves the ability of active control in terms of state space and reward function. The experimental results show that this method can not only ensure the overall traffic efficiency of short-distance intersections and reduce the travel delay but also can actively sense the change of overflow state, improve the overflow prevention and control ability of the target scenario, and reduce the overflow risk.

As the importance of safety, efficiency, and sustainability in urban transportation becomes more apparent, intelligent transportation systems are changing and growing. Smart intersections play a crucial role in different parts of this context. Technologies such as Vehicle-to-Everything (V2X) communication, artificial intelligence, multi-sensor data fusion, and more are incorporated into these intersections to improve capacity and safety and reduce damage to the environment. This literature review aims to merge various recent works on advancing smart intersection technologies, their thematic application, methodological approach, and regional implementations. Highlighting adaptive traffic signal control, real-time data processing, and connected autonomous vehicle (CAV) integrations sheds light on the way the effectiveness of transportation in cities can be improved. At the same time, this study tackles questions of cybersecurity and standardization. This review provides insights for researchers, policymakers, and practitioners who aim to improve transportation systems’ sustainability, fairness, and operability.

To encourage energy saving and emission reduction and improve traffic efficiency in the multiple signalized intersections area, an eco-driving strategy for connected and automated vehicles (CAVs) considering the effects of traffic flow is proposed for the mixed traffic environment. Firstly, the formation and dissipation process of signalized intersection queues are analyzed based on traffic wave theory, and a traffic flow situation estimation model is constructed, which can estimate intersection queue length and rear obstructed fleet length. Secondly, a feasible speed set calculation method for multiple signalized intersections is proposed to enable vehicles to pass through intersections without stopping and obstructing the following vehicles, adopting a trigonometric profile to generate smooth speed trajectory to ensure good riding comfort, and the speed trajectory is optimized with comprehensive consideration of fuel consumption, emissions, and traffic efficiency costs. Finally, the effectiveness of the strategy is verified. The results show that traffic performance and fuel consumption benefits increase as the penetration rate of CAVs increases. When all vehicles on the road are CAVs, the proposed strategy can increase the average speed by 9.5%, reduce the number of stops by 78.2%, reduce the stopped delay by 82.0%, and reduce the fuel consumption, NOx, and HC emissions by 20.4%, 39.4%, and 46.6%, respectively.

Due to a decline in psychological function, older drivers have a high incidence of crashes, especially at intersections. The study considered the characteristics of older drivers and designed a driving experiment that includes two scenarios: signal-controlled intersections and unsignal-controlled intersections. A total of 39 drivers participated in the experiment. The results indicated that compared to the young and middle-aged drivers, older drivers exhibited the following characteristics. From a time perspective, older drivers initiated decelerate and turning maneuvers earlier after entering intersections. Their overall turning process was smoother, as indicated by smaller peak steering wheel cornerings, lower steering reversal rates, and reduced lateral acceleration variability. And older drivers slowed down and turned earlier at signal-controlled intersections. From a spatial perspective, older drivers experienced a decrease in speed before entering the two intersections, followed by a sustained increase in speed and steering wheel cornerings. And there were frequent fluctuations in the speed and steering wheel cornering of older drivers at unsignal-controlled intersections. Finally, two Generalized Linear Mixed Models were developed to examine factors affecting driving stability, focusing on speed and steering control. Results showed that traffic control, driver type, and cognitive function had significant impacts. These findings enhance understanding of older drivers’ behavior and provide a reference for improving age-friendly transport systems and safety training. To better support older drivers, vehicle design, traffic signs, infrastructure planning, and management policies should consider their driving characteristics.

This study evaluates the effectiveness of Stopping for Right-Turning Large Vehicles Policy in Nanjing, designed to mitigate accidents attributed to blind spots and delayed braking of large trucks at intersections. Using high-resolution conflict data from four signalized intersections in Jiangning District, collected via unmanned aerial vehicles (UAVs) and roadside video, the research employs K-means clustering for conflict severity classification and binomial Logit regression to identify critical determinants. Results reveal the policy exhibited limited statistical significance in reducing severe conflicts (p > 0.05). Regression analysis quantified four critical determinants: absence of motorized/non-motorized segregation (OR=1.82, + 81.6% severity odds), elevated stop-line speeds (OR=1.32, + 31.9%), failure to yield (OR=2.45, + 145%), and crossing the street within the zebra crossing (OR=0.19, −81.0%). The analysis demonstrates that infrastructural deficiencies and behavioral non-compliance outweigh the policy’s standalone impact. Based on these findings, the study proposes a holistic optimization framework integrating physical separation measures, enhanced signage, dynamic traffic signal adjustments, and data-driven enforcement strategies. Methodologically, this study innovatively combines unsupervised learning for conflict categorization, providing a scalable framework for evaluating urban traffic policies. This research underscores the necessity of multi-dimensional interventions—spanning infrastructure, enforcement, and public education—to achieve sustainable improvements in intersection safety. The findings offer actionable insights for policymakers to refine regulatory measures and enhance road safety in rapidly urbanizing environments.