The time-space (TS) traffic diagram serves as a crucial tool for characterizing the dynamic evolution of traffic flow, with its resolution directly influencing the effectiveness of traffic theory research and engineering applications. However, constrained by monitoring precision and sampling frequency, existing TS traffic diagrams commonly suffer from low resolution. To address this issue, this paper proposes a refinement method for TS traffic diagrams based on neighborhood-adaptive linear regression. Introducing the concept of neighborhood embedding into TS diagram refinement, the method leverages local pattern similarity in TS diagrams, adaptively identifies neighborhoods similar to target cells, and fits the low-to-high resolution mapping within these neighborhoods for refinement. It avoids the over-smoothing tendency of the traditional global linear model, allows the capture of unique traffic wave propagation and congestion evolution characteristics, and outperforms the traditional neighborhood embedding method in terms of local information utilization to achieve target cell refinement. Validation on two real datasets across multiple scales and upscaling factors shows that, compared to benchmark methods, the proposed method achieves improvements of 9.16%, 8.16%, 1.86%, 3.89%, and 5.83% in metrics including MAE, MAPE, CMJS, SSIM, and GMSD, respectively. Furthermore, the proposed method exhibits strong generalization and robustness in cross-day and cross-scenario validations. In summary, requiring only a minimal amount of paired high- and low-resolution training data, the proposed method features a concise formulation, providing a foundation for the low-cost, fine-grained refinement of low-sampling-rate traffic data.

Connected and Automated Vehicles (CAVs) are a critical component of modern intelligent transportation systems, offering significant advantages in improving traffic efficiency, enhancing safety, and reducing energy consumption. Among these, CAV platoons have garnered considerable attention due to their ability to maintain stable spatial relationships and reduce time headway through cooperative control. Information Flow Topology (IFT), as a core element of CAV platoon performance, determines the manner in which information is transmitted between vehicles. Although bidirectional IFT demonstrates strong stability advantages in single-vehicle control, its application to CAV platoons faces numerous challenges, and significant research gaps remain. To address this research problem, this paper proposes a bidirectional distance-balancing strategy for CAV platoons, considering sensing and communication delays, which utilizes bidirectional spacing information to maintain vehicles in an equilibrium position between leading and following vehicles. Specifically, a Constant Spacing (CS) strategy is employed to control the following vehicles in the platoon, while a bidirectional information-based distance-balancing strategy is designed for the leading vehicle. Subsequently, numerical simulations are conducted in a mixed traffic flow environment to validate the effectiveness of the proposed strategy in terms of energy consumption, stability, and efficiency. The experimental results demonstrate that the proposed bidirectional distance-balancing strategy for CAV platoons exhibits excellent overall performance. Compared to single-vehicle balancing strategies, energy consumption is reduced by up to 40.81%, and the average travel speed is significantly improved. Compared to platoons without distance balancing, energy consumption is reduced by up to 4.99%, and the propagation of disturbances is better suppressed. This strategy provides a new approach for optimizing CAV platoon formation.

In an Autonomous Vehicle-Mixed Traffic Flow (AV-MTF) environment, accurately predicting vehicle speeds is essential for vehicle and traffic operation and management. However, existing research has not achieved high-precision vehicle speed prediction in mixed autonomous traffic environments. To address this, we proposed a car-following speed prediction and anomaly detection method based on the Attention-LSTM-Transformer model. We first employ a multihead attention enhanced LSTM network to dynamically classify vehicle categories in the AV-MTF environment based on vehicle motion states. We propose a novel Transformer-based model that embeds classification labels and car-following state information, enabling precise speed prediction in AV-MTF environments. Additionally, the anomaly detection algorithm is proposed to identify abnormal speeds in car-following situations, covering both constant and instant offsets. The proposed models were trained and tested using the OpenACC dataset, and the effectiveness of the Attention-LSTM-Transformer-based speed prediction model and anomaly detection algorithm is validated. Results show that the classification model achieves an accuracy of 95.57%. Compared to baseline models, the speed prediction model considering vehicle category labels effectively reduces the prediction errors by more than 6.8% in all horizons. This car-following speed anomaly detector achieves over 99% accuracy for constant speed offsets and nearly 90% detection rate for small instant speed anomalies. The findings of this study provide valuable insights for vehicle and traffic operation and management in future AV-MTF environments.

Traffic capacity is an important indicator of traffic supply capability. An accurate portrayal of intersection capacity serves as a foundation for various intersection management studies, supporting the optimization of traffic operational efficiency and resource allocation. However, existing research on autonomous intersections primarily estimates intersection capacity through simulation experiments, which often suffer from low efficiency. Consequently, the challenge lies in developing more convenient methods for capacity analysis in such environments. To address this gap, this paper proposes analytical models for the traffic capacity of autonomous intersections based on queuing theory. Firstly, the analytical model of the traffic capacity in the conflict zones is derived. Based on that, the capacity model for the autonomous intersection is developed by incorporating vehicle interaction modes, varying arrival rates, and distinct turning ratios for each approach. Depending on the settings of safety time headways, two models, referred to as the specific and uniform cases, are considered. Further, the capacity range, queuing length, and queuing time of the two scenarios are derived. Finally, the rationality and accuracy of the proposed models are verified based on numerical simulations. The results show that the proposed models can better estimate the actual operating conditions of autonomous intersections. The average errors of the autonomous intersection model in the one-lane intersection scenario are less than 2%, with a 22% error reduction from the baseline model. Besides, the average errors of the multi-lane autonomous intersection model are less than 4%. The average relative and absolute errors between the model predictions and simulation results for queuing length and queuing time are negligible. The proposed models offer valuable insights for intersection design, level of service determination, and management control strategy selection.

Autonomous intersection management has become a state-of-the-art control strategy customized for connected and autonomous vehicles. Combining the advantages of tile-based and conflict point-based approaches, this paper proposes a two-stage optimization method based on a developed intersection modeling approach. The first stage is a timing schedule optimization model, assigning vehicle arrival times at an intersection. Based on the output of the first stage, the second stage is a trajectory optimization model, which gives the eco-driving strategies. Moreover, a rolling optimization with a variable cycle length is adopted to run the method continuously. Simulation results show that the proposed method outperforms the genetic algorithm-based method in terms of computation time, and can reduce vehicle delay and fuel consumption by 89.48% and 46.84%, respectively, under different traffic demands compared to the first-come-first-serve method. Furthermore, the performance of the proposed method under asymmetric traffic demand is discussed. Sensitivity analyses suggest that (1) a long cycle length benefits the proposed method within certain limits and (2) a proper deceleration within the intersection can balance traffic delay with fuel consumption. In addition, an additional model with a heuristic rule is compared with the original timing schedule optimization model. It is found that reducing binaries in the first stage can make a tradeoff between the quality of the solution and efficiency, which can be used in conjunction with long cycles.

To solve the problems of when to set up connected automated vehicles (CAVs) dedicated lanes and how many CAVs dedicated lanes to set up under different penetration rates of CAVs, this work focuses on modeling the fundamental diagram of mixed traffic flow with dedicated lanes for CAVs. Firstly, the car-following modes and their proportion of mixed traffic flow without and with CAVs dedicated lanes are analyzed. Secondly, the fundamental diagram of mixed traffic flow is derived based on car-following models to analyze the traffic capacity with and without CAVs dedicated lanes. Then, the relevant properties of the fundamental diagram with CAVs dedicated lanes are proposed and proved. Finally, the sensitivity of related parameters (e.g., CAVs penetration rate, time headway, and free-flow speed) in the fundamental diagram is discussed. Results show that (1) the increase of CAVs penetration rate and free-flow speed can improve the traffic capacity; (2) the design of smaller CAVs time headway benefits traffic capacity. Moreover, one and two CAVs dedicated lanes of three manual lanes are set to improve the traffic capacity to the greatest extent when the penetration rate of CAVs reaches 0.6 and 0.86, respectively. It is noteworthy that developing CAVs dedicated lanes under a reasonable CAVs penetration rate does not waste resources and increases traffic congestion while improving traffic capacity.

The large-scale application of connected automated vehicles (CAVs) provides new opportunities and challenges for the optimization and management of traffic conflict zones. To improve the traffic efficiency of conflict zones and reduce the travel delay and fuel consumption of CAVs, this paper presents a two-level optimization method of scheduling and trajectory planning for CAVs. At the first level, a 0–1 mixed-integer linear program (MILP) is proposed for vehicles entering scheduling. At the second level, a multi-vehicle optimal trajectory control model is developed based on the optimal vehicle schedule from the first level. Then, to reduce the complexity of solving the multi-vehicle optimal trajectory control model, we transform this model into non-linear programming (NLP) based on the infinitesimal method. Moreover, a rolling optimization strategy is developed to facilitate field application. Numerical simulation experiments of different traffic scenarios are conducted, and the results show that the proposed method can effectively reduce vehicle delays and fuel consumption, compared with the first-in-first-out (FIFO) method. The numerical results show that the vehicle delay can be reduced by up to 54% and fuel consumption by up to 34% under different traffic demands. Sensitivity analysis indicates that the performance of the proposed method is mainly determined by the minimum safety time interval of vehicles entering the conflict zone.

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles' information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated intersections is proposed in a cross-sectional VII environment, which has the ability to predict vehicle arrivals and use this to optimize traffic signals. The proposed dynamic predictive control framework includes a dynamic platoon dispersion model (DPDM) which uses the vehicles' speed data from the cross-sectional VII environment, as opposed to traditional vehicle passing/existing data, to predict the arriving flow distribution at the downstream stop-line. Then, a dynamic programming algorithm based on the exhaustive optimization of phases (EOP) is proposed working in rolling optimization (RO) scheme with a 2s time horizon. The signal timings are continuously optimized by regarding the minimization of intersection delay as the optimization objective, and setting the green time duration of each phase as a constraint. In the end, the proposed dynamic predictive control framework is tested in a simulated cross-sectional VII environment and a case study carried out based on a real road network. The results show that the proposed framework can reduce the average delay and queue length by up to 33% and 35%, respectively, compared with the traditional full-actuated control.

As the development of traffic detection technology, recent research is directed to a new generation of signal control systems supported by new traffic data. One of these directions is dynamic predictive control by incorporating short-term prediction capability. This paper focuses on investigating dynamic platoon dispersion models which could capture the variability of traffic flow in a cross-sectional traffic detection environment. The dynamic models are applied to predict the evolution of traffic flow, and further used to produce signal timing plans that account not only for the current state of the system but also for the expected short-term changes in traffic flows. We investigate factors affecting model accuracy, including time-zone length, position of upstream traffic detection equipment, road section length, traffic volume, turning percentages, and computation time. The impact of these factors on the model's performance is illustrated through a simulation analysis, and the computation performance of models is discussed. The results show that both the dynamic speed-truncated normal distribution model and dynamic Robertson model with dynamics outperform their respective static versions, and that they can be further applied for dynamic control.