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.

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.