The COVID-19 outbreak in 2020 has changed the way people travel due to its highly contagious nature. In this study, changes in the travel behavior of passengers due to COVID-19 in the first half of 2020 were examined. To determine whether COVID-19 has affected the use of transportation by passengers, paired t-tests were conducted between the passenger volume of private vehicles in Seoul prior to and after the pandemic. Additionally, the passenger occupancy rate of different modes of transportation during the similar time periods were compared and analyzed to identify the changes in monthly usage rate for each mode. In the case of private vehicles and public bicycles, the usage rates have recovered or increased when compared to those of before the pandemic. Conversely, bus and rail passenger service rates have decreased from the previous year before the pandemic. Furthermore, it is found that existing bus and rail users have switched to the private auto mode due to COVID-19. Based on the results, traffic patterns of travelers after the outbreak and implications responding to the pandemic are discussed.

Demand responsive transport (DRT) is operated according to flexible routes, dispatch intervals, and dynamic demand, is attracting a lot of attention. The biggest characteristic of the DRT service is that the vehicle routes and schedules are operated optimally based on real-time travel requests of using passengers without fixed operating schedules. This study analyzed the feasibility of implementing the DRT service by analyzing the benefits for the users and cost of the operator from the effects of increasing public transportation use and providing personalized mobility service based on DRT implementation by the introduction of DRT using multi-agent transport simulation (MATSim). Through the simulation, the DRT is expected to provide convenient, fast, and cost-effective mobility services to customers; provide an optimal vehicle scale to providers; and, ultimately, achieve a safe and efficient transportation system.

As high-speed railways continue to be constructed, more maintenance work is needed to ensure smooth operation. However, this leads to frequent accidents involving maintenance workers at the tracks. Although the number of such accidents is decreasing, there is an increase in the number of casualties. When a maintenance worker is hit by a train, it invariably results in a fatality; this is a serious social issue. To address this problem, this study utilized the tunnel monitoring system installed on trains to prevent railway accidents. This was achieved by using a system that uses image data from the tunnel monitoring system to recognize railway signs and railway tracks and detect maintenance workers on the tracks. Images of railway signs, tracks, and maintenance workers on the tracks were recorded through image data. The Computer Vision OpenCV library was utilized to extract the image data. A recognition and detection algorithm for railway signs, tracks, and maintenance workers was constructed to improve the accuracy of the developed prevention system.

Hourly traffic volume prediction is now emerging to mitigate and respond to hourly-level traffic congestion augmented by deep learning techniques. Incorporating meteorological data into the forecasting of hourly traffic volumes substantively improves the precision of long-term traffic forecasts. Nonetheless, integrating weather data into traffic prediction models is challenging due to the complex interplay between traffic flow, time-based patterns, and meteorological conditions. This paper proposes a graph convolutional network to predict long-term traffic volume with meteorological information. This study utilized a four-year traffic volume and meteorological information dataset in Chung-ju si to train and validate the models. The proposed model performed better than the other baseline scenarios with conventional and state-of-the-art deep learning techniques. Furthermore, the counterfactual scenarios analysis revealed the potential negative impacts of meteorological conditions on traffic volume. These findings will enable transportation planners predict hourly traffic volumes for different scenarios, such as harsh weather conditions or holidays. Furthermore, predicting the microscopic traffic simulation for different scenarios of weather conditions or holidays is useful.

Demand–responsive transit (DRT) provides flexible, user-centric services and is gaining attention as a solution to modern transportation challenges. Establishing a minimum service level is crucial for its effectiveness, yet existing methods rely on supplier-centric indicators that fail to reflect user psychology and the flexible nature of DRT. To address this, this study applied a prospect theory from behavioral economics and used logistic regression analysis of stated preference survey data to determine minimum service levels based on user perceptions. To account for regional variations, we classified user groups based on primary transportation mode, travel purpose, and age, proposing dynamic minimum service levels tailored to each group. Additionally, using the maximum likelihood estimation method, we estimated value function parameters for the prospect theory, allowing us to analyze users’ loss aversion and sensitivity to DRT services. The findings indicated that users would accept higher fares for DRT than for conventional public transportation, provided it offers shorter travel times. Sensitivity to service levels varied across user groups, highlighting the need for differentiated policies. This study provides insights to optimize DRT operations, improve user satisfaction, and guide policies that reflect regional and demographic characteristics, enhancing the efficiency and effectiveness of DRT services.

Understanding the factors that affect the uptake of emerging transport modes is critical for understanding if and how they will be used once they are implemented. In this study, we undertook a stated-preference analysis to understand the factors that affect the use of shared autonomous vehicles and shared personal mobility (micromobility) as competing modes on a university campus in Korea. We applied a binary logit model, which included time and cost variables as well as the perceptions of convenience (in-car congestion and availability) and safety. For autonomous vehicles, the cost- and time-related demand elasticities were estimated to be −0.45 and −0.25, respectively, while the cost elasticity for shared electric bicycles was −0.42. The elasticities of perceived convenience (availability) and safety for the shared electric bicycle system were estimated to be 0.72 and 0.29, respectively. Finally, the elasticity for perceived convenience (in-car congestion) of the shared autonomous vehicle was 0.42. Our results show that there is an innate preference for shared autonomous vehicles when these are compared to shared personal mobility, and that the effect of subjective variables (convenience and safety) on the use of emerging transport modes is as important as traditional cost and time variables.