The unprecedented COVID-19 outbreak has significantly influenced our daily life, and COVID-19’s spread is inevitably associated with human mobility. Given the pandemic’s severity and extent of spread, a timely and comprehensive synthesis of the current state of research is needed to understand the pandemic’s impact on human mobility and corresponding government measures. This study examined the relevant literature published to the present (March 2023), identified research trends, and conducted a systematic review of evidence regarding transport’s response to COVID-19. We identified key research agendas and synthesized the results, examining: (1) mobility changes by transport modes analyzed regardless of government policy implementation, using empirical data and survey data; (2) the effect of diverse government interventions to reduce mobility and limit COVID-19 spread, and controversial issues on travel restriction policy effects; and (3) future research issues. The findings showed a strong relationship between the pandemic and mobility, with significant impacts on decreased overall mobility, a remarkable drop in transit ridership, changes in travel behavior, and improved traffic safety. Government implemented various non-pharmaceutical countermeasures, such as city lockdowns, travel restrictions, and social distancing. Many studies showed such interventions were effective. However, some researchers reported inconsistent outcomes. This review provides urban and transport planners with valuable insights to facilitate better preparation for future health emergencies that affect transportation.

This paper presents a comprehensive analysis of students’ activity characteristics and travel patterns based on the 2001 North Carolina State University Student Activity Travel Survey. Results show that undergraduate students and on-campus residents are engaged in more activities than graduate students and off-campus students. Graduate students are more likely to engage in class and work activity in the afternoon than in the morning. There is no statistically significant difference between student groups in terms of the proportion of students involved in a certain activity at a certain hour of the day. Instead, the types of activities are different across all 24 h of a day. The insights gained from this study will serve as the basis of an activity-based university travel demand model for North Carolina State University.

Rapid demographic ageing is a global challenge and has tremendous implications for transportation planning, because the mobility of elderly people is an essential element for active ageing. Although many studies have been conducted on this issue, most of them have been focused on aggregated travel patterns of the elderly, limited in spatiotemporal analysis, and most importantly primarily relied on sampled (2–3%) household travel surveys, omitting some trips and having concerns of quality and credibility. The objectives of this study are to present more in-depth analysis of the elderly’s spatiotemporal activity and travel behaviors, to compare them with other age and gender groups, and to draw implications for sustainable transportation for the elderly. For our analysis, we used locational trajectory-based mobile phone data in Gangnam, Korea. The data differs from sampled household travel survey data, as mobile phone data represents the entire population and can capture comprehensive travelers’ movements, including peculiarities. Consistent with previous researches, the results of this study showed that there were differences in activity and travel patterns between age and gender groups. However, some different results were obtained as well: for instance, the average nonhome activity time per person for the elderly was shorter than that of the nonelderly, but the average numbers of nonhome activities and trips were rather higher than those of nonelderly people. The results of this study and advantage of using mobile phone data will help policymakers understand the activities and movements of the elderly and prepare future sustainable transportation.

This study analyzed the performance of transit services using data collected from Automatic Fare Collection (AFC) system in Seoul with respect to service measures such as schedule adherence of metro, occupancy (crowdedness), and the operational speeds of buses. In order to analyze the transit services performances, we developed data-mining logics and applied them to a case study. These logics successfully indicate the route segment with a low Level of Service (LOS) for metro punctuality and bus operational speeds during peak hours (i.e., 7 a.m. to 9 a.m.). Further, a relationship between vehicle occupancy and punctuality on certain transit route segments is easily found based on the logical analysis. These empirical applications are beneficial to transit agencies and planners for improving transit service performances by using massive amount of data.

In this study, using public transport smart card data observed in Seoul, Korea, we provide empirical evidence related to the impact of the opening of a new railway line on passenger demand changes at transfer stations. An intervention time series modeling framework was applied to evaluate the travel demand patterns based upon massive card data collected before and after a new line opened. The results show that the new line opening has had meaningful intervention effects on passenger demand, but significant changes were not observed at all stations. This implies that the trends have been changed structurally and the changes may last for a long time. Consequently, it is found that the new line opening changed the trends of passenger ridership and these changes can be successfully explained by various intervention patterns derived from extensive Automatic Fare Collection (AFC) data. The intervention analysis will provide valuable information to transit agencies to enable them to make better decisions related to transit planning, pricing, and the deployment of new transit services.