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.

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, and communications remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT’s scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Lastly, the DT’s ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the safety and mobility of transportation systems.

Cellular vehicle-to-everything (C-V2X) is an emerging technology that has the potential to significantly improve traffic safety and efficiency through various applications such as pedestrian alerts, traffic hazard warnings, signal timing information broadcasting, etc. With the help of the Federal Communications Commission's agreement to waive restrictive rules on C-V2X deployment, extensive real-world testing and implementation of C-V2X is on the horizon. However, the performance of C-V2X in urban environments remains unclear as existing studies have been focusing on evaluating it in either simulated or highway environments. To facilitate the understanding of its performance in urban environments, this study has conducted a systematic evaluation of the efficiency and reliability of C-V2X using various metrics based on real-world data collected through roadside units (RSUs) and onboard units in urban environments. Experimental results indicate that C-V2X has relatively stable efficiency (i.e., latency) under different conditions. However, its reliability (i.e., packet error rate and spatial delivery rate) is significantly affected by multiple factors, including the distance from the vehicle to the RSU and to its surrounding buildings. Moreover, this study has proposed a concept of machine learning-assisted RSU planning to facilitate the planning and deployment of C-V2X in urban areas. An XGBoost model is developed to accurately estimate the probability of the delivery of V2X messages using C-V2X. Results from explaining the model suggest that the planning of RSUs should consider the spacing of RSUs and the density of buildings in the area.

Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

Most existing cellular probe-based freeway congestion detection methods rely on on-call WLT (Wireless Location Technologies) signal transition data. However, these techniques facing difficulties such as small sample size, frequent road tests, safety, and privacy issues. This article presents a novel approach using the FCA data for traffic congestion detection on freeways. Two cellular activity features, the link pseudo speed and link probe activity, are defined and calculated. A rule-based algorithm is then developed to determine the traffic congestion state. The proposed method has been implemented and a prototype system has been deployed for a major freeway corridor in China. Validated by fixed-point detector data and incident records, the proposed method is able to identify real-time freeway traffic congestion accurately.