The introduction of Connected and automated vehicles (CAVs) promises various social and economic benefits, but safety issues are encountered when CAVs are mixed with human‐driven vehicles (HDVs). This study presents three scenarios as lane operation alternatives for situations in which CAVs and HDVs are mixed. The proposed lane operation scenarios for CAVs are the operation of dedicated lanes (DLs) for CAVs, operation of a DL for CAVs and buses, and the operation of DLs for buses. For each scenario, the effects of the market penetration rate (MPR) on traffic efficiency and safety were evaluated. The following findings were obtained from the results: CAVs had a potential effect on each lane according to the lane operation scenario; traffic efficiency and safety improved in a traffic flow situation where CAVs and HDVs were mixed; traffic efficiency and safety were higher when operating DLs for CAVs than when operating a mixed traffic flow; and efficiency and safety were optimal when operating Scenario 2 (DL for CAVs and Buses) in the early stages of introducing CAV (MPR 10–30%) and operating Scenario 1 (DL for CAVs) in the mid‐ to long‐term stages (MPR 40–80%). These findings provide a basis for preparing new policy alternatives to the current policy of adopting DLs for buses. Furthermore, the evaluation of the effects of each DL operation in the transition period where CAVs and HDVs are mixed will contribute to the operation of an efficient and safe CAV‐based road traffic system.

The unstructured-textual crash descriptions recorded by police officers is rarely utilized, despite containing detailed information on traffic situations. This lack of utilization is mainly due to the difficulty in analyzing text data, as there is currently no innovative methodology for extracting meaningful information from it. Given limitations and challenges in analyzing traffic crash descriptions, this study developed a methodology to classify significant words in unstructured data that describe traffic crash scenarios into standardized data. Ultimately, a natural language processing technique, specifically a bidirectional encoder representation from transformer (BERT), was used to extract meaningful information from crash descriptions. This BERT-based model effectively extracts information on the exact collision point and the pre-crash vehicle maneuver from crash descriptions. Its practical approach allows for the interpretation of traffic crash descriptions and outperforms other natural language processing models. Importantly, this method of extracting crash scene information from traffic crash descriptions can aid in better comprehending the unique characteristics of traffic crashes. This comprehension can ultimately aid in the development of appropriate countermeasures, leading to the prevention of future traffic crashes.

Mobility services facilitate various tasks related to transportation and passenger movements. Because of the Fourth Industrial Revolution, the importance of mobility services has been recognized by many countries. Thus, research is ongoing to provide more convenience to passengers and to obtain more efficient transportation systems. In the Republic of Korea, the officials of Gyeonggi Province are interested in providing an advanced mobility service to its residents; however, they still do not have any specific or detailed policies. This study aimed at deriving the key issues facing mobility services, especially in the case of Gyeonggi Province, by using a text mining technique and a clustering algorithm. First, a survey was taken by traffic and urban experts to collect reasonable plans for Gyeonggi-Province-type mobility service, and a morpheme analysis was then used for text mining. Second, the results reveal that the term frequency–inverse document frequency (TF-IDF) algorithm has better performance than frequency analysis. Third, the K-means application results in six clusters and six mobility service policy issues were determined by combining the words in each cluster. Finally, the methodology confirmed the validity and effectiveness of the proposed method by showing that the results reflect the current situation in the province.

The transportation-disadvantaged population is rapidly increasing through aging; moreover, the mobility of the elderly has increased due to life expectancy improvements and lifestyle changes. To respond to these changes, a customized mobility service specifically tailored to the travel patterns of the elderly was developed in this study. In particular, an autonomous shuttle service that can improve the operational efficiency and the accessibility of existing transportation methods was proposed by considering the travel characteristics of elderly people, who mainly travel short distances. To this end, a study was conducted in Seongnam City, Republic of Korea, where mobility support services for the elderly are insufficient. Using smart card data, the elderly were classified according to their travel patterns, with autonomous shuttle routes suggested for each travel purpose. We derived four clusters via the Gaussian mixture model clustering, with travel purposes classified according to the spatiotemporal travel patterns. Finally, the major routes for each travel purpose were selected, and feasible road paths for autonomous shuttle operation were suggested using the concept of ODD (operational design domain). This holistic methodology is expected to contribute to the development of autonomous mobility services for the elderly and for the establishment of welfare policies based on the smart card data.

This study aims to develop a spatiotemporal traffic density estimation method based on the advanced driver assistance system (ADAS) Probe data. This study uses the vehicle trajectory data collected from the ADAS equipped on the sample probe vehicles. Such vehicle trajectory data are used firstly to estimate the distance headway between the vehicles on a specific road section, and the postprocessed distance headway data are finally used to estimate the spatiotemporal traffic density. The innovation aspect of the proposed methodology in this study is that traffic density can be estimated in high accuracy only with a small size of data points in support of ADAS. On the other hand, existing density estimation method requires a large number of probe vehicles and its numerous data sets including either the global positioning system data or the dedicated short-range communication data. To verify the proposed methodology, a two-step evaluation is performed: the first step is a numerical evaluation that estimates the spatiotemporal traffic density based on the simulated vehicle trajectory data, and the second step is an empirical evaluation that estimates the density based on the real-road data in both peak and nonpeak periods. Beyond the methodology development, this study verified the estimation reliability of traffic density under various traffic conditions based on the sampling rate of ADAS-equipped vehicles. Consequently, the traffic density estimation error decreased as the sampling rate increased. Estimation accuracy of 90% or higher was observed in all scenarios when the sampling rate was 50% or higher. It indicates that fairly accurate traffic density estimation is feasible using probe vehicles that correspond to half of the vehicles driven on the road. Therefore, this practical approach is expected to mitigate the burden of density estimation, particularly in future road systems in which ADAS and autonomous vehicles are prevalent.

In this study, we propose a methodology for driving pattern analysis based on a hybrid clustering algorithm that combines the self-organizing map (SOM) and the K-means++ algorithm. The methodology aims to identify automated vehicle (AV) driving behaviors, highlight road sections with risky behaviors, and evaluate these behaviors using real-world driving data from automated shuttle operations. By employing a hybrid clustering algorithm grounded in Artificial Neural Network (ANN) principles, the approach enhances reliability compared to traditional clustering techniques. The analysis identifies both normal and risky driving behaviors, delineating specific driving patterns and visualizing them on maps. This enables the development of adaptable models for various AV types. Applied in the Pangyo area, the methodology reveals six distinct clusters, uncovering three risky behaviors and six unique driving patterns. This practical approach supports monitoring AV behaviors and improving road safety in mixed-traffic environments with AVs and conventional vehicles. Moreover, the methodology is adaptable to any region with high-definition (HD) map data, offering valuable insights for automated driving traffic control centers.