After the COVID-19 pandemic, a decrease in travel demand due to the mass transport characteristics of public transit was inevitable, and the impact of the pandemic has increased the need for new transport policies. This study aims to propose transport policies for public transit through comparison by quantitatively measuring passengers’ crowding impedance before and after the COVID-19 pandemic. The crowding impedances are evaluated through a random parameter mixed logit model from the two surveyed data to compare the behavioral differences before and after the COVID-19 pandemic. The behavioral differences are compared using crowding multipliers, and, accordingly, the results show that crowding impedances after the COVID-19 pandemic are about 1.04 ~ 1.23 times higher than before the COVID-19 pandemic. Transport policies for transit policymakers and operators are proposed to cope with the crisis in public transportation caused by an infectious disease.

This study analyzes 86,622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1,875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company's type of vehicle ownership and municipality's level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.

Obtaining the trajectories of all vehicles in congested traffic is essential for analyzing traffic dynamics. To conduct an effective analysis using trajectory data, a framework is needed to efficiently and accurately extract the data. Unfortunately, obtaining accurate trajectories in congested traffic is challenging due to false detections and tracking errors caused by factors in the road environment, such as adjacent vehicles, shadows, road signs, and road facilities. Unmanned aerial vehicles (UAVs), with incorporating machine learning and image processing, can mitigate these difficulties by their ability to hover above the traffic. However, research is lacking regarding the extraction and evaluation of vehicle trajectories in congested traffic. In this study, we propose and compare two learning-based frameworks for detecting vehicles: the aggregated channel feature (ACF), which is based on human-made features, and the faster region-based convolutional neural network (Faster R-CNN), which is based on data-driven features. We extend the detection results to extract vehicle trajectories in congested traffic conditions from UAV images. To remove the errors associated with tracking vehicles, we also develop a postprocessing method based on motion constraints. Then, we conduct detailed performance analyses to confirm the feasibility of the proposed framework on a congested expressway in Korea. The results show that Faster R-CNN outperforms the ACF in images with large objects and in those with small objects if sufficient data are provided. This framework extracts the vehicle trajectories with high precision, making them available for analyzing traffic dynamics based on the training of just a small number of positive samples. The results of this study provide a practical guideline for building a framework to extract vehicles trajectories based on given conditions.

Bus–pedestrian crashes typically result in more severe injuries and deaths than any other type of bus crash. Thus, it is important to screen and improve the risk factors that affect bus–pedestrian crashes. However, bus–pedestrian crashes that are affected by a company’s and regional characteristics have a cross-classified hierarchical structure, which is difficult to address properly using a single-level model or even a two-level multi-level model. In this study, we used a cross-classified, multi-level model to consider simultaneously the unobserved heterogeneities at these two distinct levels. Using bus–pedestrian crash data in South Korea from 2011 through to 2015, in this study, we investigated the factors related to the injury severity of the crashes, including crash level, regional and company level factors. The results indicate that the company and regional effects are 16.8% and 5.1%, respectively, which justified the use of a multi-level model. We confirm that type I errors may arise when the effects of upper-level groups are ignored. We also identified the factors that are statistically significant, including three regional-level factors, i.e., the elderly ratio, the ratio of the transportation infrastructure budget, and the number of doctors, and 13 crash-level factors. This study provides useful insights concerning bus–pedestrian crashes, and a safety policy is suggested to enhance bus–pedestrian safety.