This study presents a novel integrated framework for autonomous vehicle control at unsignalized intersections in mixed traffic environments, addressing the critical challenge of coordinating Society of Automotive Engineers (SAE) level 4 connected and autonomous vehicles (CAVs) and manually driven vehicles (MVs). The combination of driving intention prediction with a Social Long Short-Term Memory (Social LSTM) and a scheduling algorithm with optimization-driven Pass-through Time Windows (PTWs) is adopted to address traffic flow uncertainty. The Social LSTM model with spatial pooling layers to capture complex multi-vehicle interactions and predict surrounding vehicles’ trajectories and maneuver intentions using naturalistic driving data from the CitySim dataset was applied. Unlike conventional approaches that treat prediction and control separately, this framework leverages high-confidence trajectory predictions to inform proactive scheduling decisions for conflict mitigation. The PTW scheduling algorithm formulates intersection management as a constrained optimization problem, dynamically allocating non-overlapping temporal windows for vehicle entering and exiting while considering vehicle dynamics, safety gaps, and deceleration constraints. Comprehensive simulation analysis across varying traffic volumes and CAV market penetration rates reveals significant improvements in both safety and operational efficiency. The scheduling algorithm has notably reduced traffic delay times while maintaining balance with safety measures. This finding provides a fundamental basis for infrastructure-based cooperative driving research, serving as a contributing factor for the development of advanced traffic management systems during the mixed-traffic period.

Korea operates exclusive bus lanes (XBLs) on many of its expressways. As XBLs convert one lane of regular-use traffic into bus-only traffic, they have a large impact on traffic flow, so careful judgment is required to determine if the operation is effective. However, XBLs have been operated based on only political judgment due to the lack of standards for the operation of expressway XBLs. Therefore, we sought to establish standards for the operation of expressway XBLs using the micro-traffic simulation program VISSIM and the multi-criteria value function methodology. Various scenarios were established based on traffic volume changes of passenger vehicles and bus traffic of four- and five-lane expressway networks. Through an expert survey, the average speed and speed-deviation values, which are the criteria for evaluating operational efficiency and safety, were determined. Also, value points were converted using average speed and speed deviation extracted from the simulation. In addition, quantitative operation standards were established using the converted value scores. Using the results of this study, we established standards for the operation of the XBLs and presented guidelines for related agencies such as police, bus groups, and corporations. The National Police have prepared guidelines for the operation of the XBLs. Citing the results of this study, the new guidelines were implemented in February 2021, and sections of some XBLs have been abolished. Through this study, quantitative standards for the operation of XBLs, one of the management lane techniques necessary for sustainable highway operation, were prepared and applied to actual highways. By properly applying the newly applied guidelines according to quantitative standards, there will be effects of reducing traffic congestion, improving travel time, and enhancing environmental characteristics such as exhaust gas emission. It is also expected to have a positive effect on safety.

The factors that affect the severity of crashes must be identified for pedestrian and traffic safety in urban roads. Specifically, in the case of urban road crashes, these crashes occur due to the complex interaction of various factors. Therefore, it is necessary to collect high-quality data that can derive these various factors. Accordingly, this study collected crash data, which included detailed crash factor data on the huge urban and mid-level roads. Using this, various crash factors including driver, vehicle, road, environment, and crash characteristics are constructed to develop a crash severity prediction model. Through this, this study identified more detailed factors affecting the severity of urban road crashes. The crash severity model was developed using both machine learning and statistical models because the insights that can be obtained from the latest technology and traditional methods are different. Therefore, the binary logit model, a support vector machine, and extreme gradient boosting were developed using key variables derived from the multiple correspondence analysis and Boruta-SHapley Additive exPlanations. The main result of this study shows that the crash severity decreased at four-street intersections and when traffic segregation facilities were installed. The findings of this study can be used to establish a traffic safety management strategy to reduce the severity of crashes on urban roads.