Reliable traffic-signal information delivery is critical for safe navigation through signalized intersections, particularly for low-cost autonomous vehicles that rely on Vehicle-to-Network (V2N) communication rather than on-board HD maps or expensive perception sensors. Ensuring this selective delivery requires accurate infrastructure-side map-matching, which becomes challenging when vehicles operate with only Standard Definition (SD) maps and noisy GNSS measurements. This study comparatively evaluates five infrastructure-side map-matching algorithms under varying GNSS errors and road-network densities using real trajectories from Jeju Island with controlled Gaussian perturbations. The framework includes geometric matching, Extended Kalman Filtering (EKF), route-constrained filtering, grid-based spatial indexing, and a hybrid route–EKF fallback mechanism, executed in real time on a cloud-hosted Kafka pipeline. The hybrid route–EKF algorithm exhibited consistently high and stable link-matching accuracy (0.99308–0.96546 across GPS error groups; 0.9887–0.9777 across density groups) together with strong signal-matching accuracy (0.99394–0.96950; 0.9865–0.9790). Route-constrained and Kalman-based approaches also performed well, while heading-based matching showed clear limitations. These results indicate that infrastructure-side map-matching provides a scalable foundation for cloud-assisted traffic-signal information services and supports the feasibility of delivering reliable traffic-signal information to low-cost autonomous platforms.

Currently, the level of automated driving technology is level 3 service in Korea, and the level 4 service has been launched overseas and is reaching the stage of commercialization. For commercialization, it is necessary not only to stabilize automated driving technology but also to prepare for service applications. To apply and expand automated driving services, it is most urgent to secure the safety of automated driving technology, and it is necessary to prepare for efficient procedures and methods suitable for automated driving services. In this study, considering the two aspects of AV manufacturer and service provider, the spatial factors affecting the safety of ADS are estimated using disengagement information in the level 3 service field data in Korea, and the method is proposed to select the automated driving service sections using spatial information analysis. To validate the method, the matching rate is compared and analyzed by applying it to the other testbed, which is currently being prepared for the level 4 car sharing service. According to validation results, it judged that the proposed method could be used to select the automated driving service section efficiently in the planning stage before deploying AVs and providing services.

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems.

Despite the increasing deployment of bus dedicated lanes in practice, commonly available programs for arterial signal plans are not designed to provide concurrent progression for such bus flows and general traffic. This study presents a bandwidth maximization model for traffic engineers to design both the phase sequences and offsets over a series of intersections along an arterial with bus dedicated lanes. The proposed model, offering concurrent progression to both modes based on traffic volumes and geometric conditions, has considered the following factors: bus dwelling times, location of bus dedicated lanes, traffic queues, and conflicts between the through bus movements and left‐turning traffic. In addition, by weighing the bandwidths with the passenger volumes by mode and by direction, the proposed model offers progression to the mode(s) and the direction(s) for which it is justified from the perspective of maximizing the benefits to all arterial users. The results from numerical analysis with actual arterial and field data have confirmed the effectiveness of the proposed model in producing concurrent progression bands for both modes under various realistic constraints. Given reliable information for key input variables, the proposed model can serve as an effective tool for traffic professionals to do the essential design task.

Autonomous vehicles (AVs) at SAE Levels 3 require a take-over request to switch from autonomous to manual mode when leaving the operational design domain (ODD). An appropriate take-over request lead time (TORlt) is necessary for safe interaction between AVs and non-AVs. This study developed a methodology to derive the optimal TORlt for AVs entering the area out of the ODD using a multi-agent driving simulator experiment. The multi-criteria decision-making method was adopted to integrate evaluation indicators to derive an optimal TORlt. The TORlt was defined as 3, 6, 9, 12, and 15 s in the driving simulation experiment scenario. The driving simulation experiment was conducted with a total of 60 participants. The simulation network was a two-lane urban road in each direction with a total length of 1.7 km, including a school zone where the autonomous driving mode is prohibited. Three requirements were established to determine the optimal TORlt: minimizing the take-over time, maximizing the success rate of take-over, and minimizing the potential of rear-end collisions due to vehicle interactions. After conducting comparative analyses of individual evaluation indicators for each scenario, a multi-criteria decision-making method was used for integrated evaluation to determine the optimal TORlt. It was found that the optimal TORlt for AVs on urban roads is 9 s. The results of this study can be used as valuable fundamentals in determining take-over requests for AVs toward safer vehicle interactions in the traffic stream.