This study evaluated the effects of COVID-19 on attitudes toward residential associated with travel behavior on decisions regarding future relocation. Chi-square automatic interaction detection was used to generate tree and classification segments to investigate the various segmentations of travelers and residents around mass transit stations. The decision tree revealed that the most influential variables were the number of transport card ownerships, walking distance to the nearest mass station, number of households, type of resident, property ownership, travel cost, and trip frequency. During the COVID-19 pandemic, people have concentrated on reducing travel time, reducing the number of transfers, and decreasing unnecessary trips. Consequently, people who live near mass transit stations less than 400 and 400–1000 m away prefer to live in residential and rural areas in the future. Structural Equation Modeling was used to confirm the relationship between attitudes in normal and pandemic situations. According to the findings, attitudes toward residential accessibility of travel modes were a significant determinant of attitudes toward residential location areas. This research demonstrates travelers’ and residents’ uncertain decision-making regarding relocation, allowing policymakers and transport authorities to better understand their behavior to improve transportation services.

Recently, the rapid climate change caused by increasing CO2 emissions has become a global concern. Efficient transportation systems are necessary to reduce CO2 emissions in cities. Taxi services are an essential part of the transportation system, both in urban areas with high demand and in rural areas with inadequate public transportation. Inefficient taxi services cause problems such as increased idle times, resulting in increased CO2 emissions. This study proposes a taxi allocation model that minimizes taxi idle time costs for efficient taxi service operation. We also propose three heuristic algorithms to solve the proposed model. At last, we conduct a case study by using real taxi data in Nagaoka, Japan. By comparing the three algorithms, the dynamic greedy algorithm produced the best result in terms of idle time cost and CPU time. The findings indicate that by minimizing idle time costs and reducing the number of taxis, it is possible to achieve a significant 81.84% reduction in CO2 emissions within the transportation sector. Further, in order to estimate the idle time costs the sensitivity of demand is considered.

This study examines the relationship between travel modes and the attitudes of residents and travelers around mass transit stations. The importance of this study was emphasized by considering that the attitudes toward residence could affect future travel and relocation considerations. In particular, the outbreak of COVID-19 may have a significant effect on their relationship. To investigate the direct and indirect effects before and during the COVID-19 pandemic, a moderated mediation model was used to test the hypothesis of this study by three-step approach analysis. The attitude toward residence was defined to test the hypothesis of the mediator, and the walking distance to the nearest mass transit station was employed to identify the level of the moderator. The results indicated that the attitude toward residence mediated the relationship between the attitude toward travel mode and travel mode behavior. The sensitivity of COVID-19 accurately reflects the various effects on travel mode. Moreover, multi-group analyses show that walking distance moderators have a direct effect on attitudes toward travel mode and travel mode behavior as well as the attitude toward residence.