This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle's acceleration and steering angle. The statistical analysis of the model's parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this strong correlation should not be ignored if one is interested in the vehicle's microscopic motion behavior. We further develop a driving risk potential field-based car-following model (DRPFM) to remedy the failure of acceleration consideration under the conventional environment, whose parameters are calibrated by filtered I-80 NGSIM data with frequent traf?c oscillations. Simulation results indicate that our proposed DRPFM model is proved to be a good description of car-following behavior and outperforms two classical car-following models (Optimal Velocity Model and Intelligent Driver Model) in frequent oscillation phases due to our consideration of potential acceleration data acquisition in real-time under the CAVs environment. In addition, this DRPFM model is applied to deduce the safety conditions for vehicle lane-changing. The analysis results prove that this model can reasonably explain the influencing factors between driver types and lane-changing safety conditions in practice.

In the conventional variable speed limit (VSL) strategy, the control area is fixed under confined conditions. With the facility of the roadside unit, the control area for the VSL will vary in real-time in the connected environment. This study proposed an extended model-based VSL controller to improve traffic efficiency in the connected environment. The controller was designed based on the scheme of model predictive control (MPC). In the controller, an extended cell transmission model (CTM) with variable-length cells was established. The cell length variation was introduced to describe the characteristics of the variable control area. The optimizer in MPC is an improved Genetic Algorithm. A numerical simulation was conducted to show how the method, with the cooperation of variable speed limit and variable control area, alleviates the shock waves. The performance of the method was compared with a conventional VSL without a variable control area. The results show that the extended model-based VSL controller reduces the total travel time by 14.57% compared with the conventional VSL controller. The compared results illustrate that the proposed VSL controller can effectively resolve shock waves produced by the incident.

In the literature, macroscopic fundamental diagram (MFD)- or network fundamental diagrambased perimeter control approaches have been demonstrated as cost-effective means to improve traffic efficiency for urban traffic networks. This article proposes a new perimeter control approach, which extends previous approaches in two aspects. First, the proposed approach optimizes the allocation of perimeter metered flow considering the heterogeneity of network traffic flow distribution, which is inversely related to network production. Second, queue management is considered by the proposed approach so as to prevent queue spillback induced by perimeter control. The proposed control approach is well advanced for field application using only loop detector data. It adopts a hierarchical framework, where the upper level controller determines the total flow entering the protected network through perimeter links based on an MFD-based feedback regulator, while the lower level controller optimizes the distribution of the total metered flow among the perimeter links based on real-time network heterogeneity and perimeter queues. The proposed approach is tested and compared with state-of-the-art perimeter control approaches, using a real traffic network developed in the microscopic simulation software VISSIM. Results demonstrate that the proposed approach significantly reduces the overall network delay. Moreover, it outperforms other perimeter control approaches by maintaining low network heterogeneity within the protected network and preventing (or delaying) queue spillback at perimeter links.

Vehicle trajectory data is essential for analyzing and modeling complex traffic behaviors. Although extraction of vehicle trajectory from aerial video data is not a new problem, obtaining trajectories with heading information across various road types, such as intersections or long road segments, requires further research. In this paper, we propose OpenVTER, a generalized Open-source Vehicle Trajectory Extraction framework based on Rotated bounding boxes (RBBs). This framework includes several key components: video stabilization, image division, vehicle detection, vehicle tracking, and data post-processing. Specifically, the rotated vehicle detection model, named YOLOX-R, is applied to detect the small and rotated vehicles using RBBs that provide vehicle heading information. A base-frame video stabilization method is proposed to reduce error accumulation in the transformation matrix and improve the computational efficiency. The rotated vehicle tracking model, named SORT-R, is proposed to enable real-time tracking of RBBs. The performance of YOLOX-R is evaluated on two datasets, showing that vehicle detection challenges are well addressed. Ablation experiments were also conducted to analyze the effectiveness of different modules. Subsequently, we evaluate the completeness of the extracted trajectories under various road types and lighting conditions. The extracted trajectories are also compared with the NGSIM dataset, focusing on internal and platoon consistency. These evaluations demonstrate both the effectiveness and practicality of the proposed framework. Additionally, the visualization analyses of different road types demonstrate the advantages of the trajectories extracted by OpenVTER in various road scenarios for traffic research. The code and dataset are available online for non-commercial research purposes.

Vehicles conduct mandatory lane changes to merge into the open lanes in temporary highway work zones, and the merging characteristics have been widely studied over the past decades. Unlike research on other traffic facilities, the data and scenarios of highway work zones are often lacking and monotonous. This study presented a vehicle trajectory dataset in highway work zones. The dataset contains ten work zone sites and three different layouts. After transforming raw videos into high-resolution trajectories, we selected eight areas and divided them into three scenarios to investigate the merging characteristics in highway work zones with different traffic states and layouts. In work zones with varying traffic conditions, we found opposite merging locations and lane speed distribution trends. In work zones with varying layouts, we found different merging location distributions, lane speed distributions, and impacts on the following vehicle’s speed. The relationship between the gap and merge speed was consistent with previous studies.