As a key technology to alleviate urban congestion, Cooperative Adaptive Cruise Control (CACC) requires testing and evaluation before being commercialized. To evaluate CACC, simulation plays a key role. However, conventional simulation platforms focus more on highway scenes but less on urban scenes. Hence, this paper proposes a CACC simulation platform for urban scenes. The platform bears the following features: i) able to plug in production Connected and Automated Vehicle (CAV) control systems; ii) able to capture the interaction between Human-driven Vehicles (HVs) and CAVs; iii) able to evaluate CACC with regard to various traffic control; iv) capable of quantifying CACC safety in urban scenes; v) capable of assessing the impact of CACC on mobility in urban scenes. The platform is validated by comparing against actual field tests and previously published theoretical studies. Its credibility is confirmed in terms of free flow CACC, interrupted CACC flow and free flow Partially Connected and Automated Traffic (PCAT). Through the proposed platform, evaluations on CACC safety and mobility are conducted. The results reveal that current CACC technology requires significant enhancement, since CACC technology may interrupt the traffic in urban areas due to platoon splitting, interaction with lane-changing vehicles, etc. Rushing the deployment of current CACC technology in urban areas is probably not a good idea.

Truck platooning is a promising technology in freight transport. To commercialize truck platooning as early as possible, its evaluation is in urgent need. For truck platooning evaluation, simulation platforms play a crucial role. However, there has not been a simulation platform to meet the evaluation needs of various stakeholders, including Original Equipment Manufacturers (OEMs), Freight Operators (FOs) and Transportation Management Administrations (TMAs). To fill the research gap, this paper proposes a next-generation simulation platform. It integrates a traffic simulator, platoon management system, and truck control module to satisfy all the evaluation needs. The proposed platform bears the following features: i) Compatibility with various platooning decision makers, planners, controllers, vehicle types and platoon management strategies; ii) Capability of evaluating platoon performance on the lateral dimension; iii) Prototype platoon management system provided for FOs; iv) Capability of evaluating truck platoon management performance in terms of sustainability and economy; v) Capability of evaluating the impact of interactive background traffic on platoon performance. vi) Capability of evaluating the impact of truck platoon management on traffic mobility. The proposed platform is validated by comparison against an actual field test. Its credibility is confirmed in terms of truck platoon performance and interactive traffic simulation. Additional tests are conducted to evaluate truck platoon performance and the impact of truck platoons on mixed traffic. The results reveal that existing platoon lane-change technologies should be upgraded to be compatible with high-traffic-demand scenarios. It is also revealed that a localized and up-to-date assessment is required before allowing truck platooning.

Summary form only. Presents a summary of articles presented in this issue of the publication.

Perception technology plays a crucial role in vehicle automation, yet traditional approaches solely rely on onboard computing and have inherent limitations in perception range. To improve perception range, edge computing is introduced. Through edge computing, some perception computing tasks can be offloaded from onboard sensors to sensors installed on roadside infrastructures. This Infrastructure Enabled Perception (IEP) expands perception range beyond what onboard sensors alone can achieve. However, existing IEP systems have limited precision in long-distance perception and require costly sensors for optimal performance. To enhance the performance without significant financial investment, this paper proposes an enhanced IEP system. The proposed IEP system adopts a virtual-detector-based perception solution, designing multiple virtual detectors to detect vehicle arrivals. Unlike traditional IEP approaches, the proposed system does not rely on dense data points for estimating vehicle geometry. Instead, it bypasses the geometry-estimation step and only needs sparse data points to detect vehicle arrivals. Consequently, even with sparse data points at long perception distances, our IEP system can achieve high precision in long-distance perception. Due to this enhanced solution, the proposed IEP system has the following features: i) Ensuring wide perception range; ii) Enabling cm-level precision perception; iii) Maintaining robustness against perception distances, vehicle speeds, and sensor frequencies; iv) Compatible with mass-produced and costeffective sensors; v) Laying a foundation for infrastructure-enabled cooperative driving. Experimental validation confirms the systems advanced features and demonstrates its superiority over the state-of-the-art IEP system. It achieves a wide perception range up to 150 meters and a low localization error down to 6.500 centimeters. Further investigation suggests that the IEP system should primarily be deployed on expressways and implemented for speed-related cooperative driving applications, such as speed harmonization.

Bus priority is an effective way to improve traffic efficiency and sustainability. To achieve this, the Bus Priority Lane (BPL) is adopted to provide exclusive right-of-way for buses. However, the BPL is underutilized if the frequency of buses is low. To address this issue, many studies focus on improving the BPL’s utilization efficiency by intermittently allowing general vehicles to access it. However, these studies still have some shortcomings: (i) bus priority cannot be guaranteed if general vehicles run on the BPL; and (ii) the traffic system lacks resilience, especially when the traffic demand is unbalanced. This paper proposes a dynamic right-of-way allocation for the BPL, considering traffic system resilience. On the one hand, it ensures absolute bus priority by controlling Connected Automated Vehicles (CAVs), so as they do not interfere with buses. On the other hand, it can improve traffic system resilience by allocating right-of-way for CAVs with heavy turning-movement demand. To test the effectiveness, the proposed control strategy is compared with the non-control baseline. The experiments are conducted under seven unbalanced-traffic-demand levels, four congestion levels, and five CAV Penetration Rates. The results show that the proposed strategy can ensure absolute bus priority and improve traffic efficiency and traffic system resilience.

The Dedicated Bus Lane (DBL) is often adopted to ensure transit priority. This is because transit priority can effectively mitigate congestion at the signalized intersection. However, the DBL would cause heavy sacrifices from general vehicles when the frequency of buses is low. To address this issue, many studies were proposed to reduce general vehicles’ sacrifice by converting DBLs into Bus‐Priority Lanes (BPLs). Such BPLs can be intermittently open to general vehicles. However, these studies cannot ensure absolute transit priority when general vehicles access BPLs. With the advance of Connected Automated Vehicle (CAV) technology, this paper proposes a Trajectory‐Based Control (TBC) method for connected automated traffic to approach signalized intersections considering absolute transit priority. A TBC controller is designed to control general vehicles’ trajectories to access BPLs without interference with buses. The TBC controller can balance the multiple cost factors and ensure absolute bus priority. The proposed TBC controller is evaluated against the noncontrol baseline and the state‐of‐the‐art TBC. Sensitivity analysis is conducted under four different congestion levels. The results demonstrate that the proposed TBC method outperforms and has benefits in improving throughputs and fuel efficiency and reducing delays.