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.

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 used mobile phone data to examine the spatiotemporal patterns and preferences of domestic visitors to Jeju Island, South Korea and integrated hourly floating population data to examine visitor characteristics by season, day of the week, time of the day, and inflow regions; identified hotspots at the census block level; and involved spatial autocorrelation analysis. The primary findings indicate that visitor numbers vary significantly by season, with summer and winter seasons attracting the highest number of tourists. The analysis revealed a concentration of visitors around Jeju Airport and the Jungmun Tourist Complex, suggesting that these are key hotspots. The inflow analysis underscores a dominant influx from major urban centers, particularly Seoul and Gyeonggi Province. The Global Moran’s Index confirmed a positive spatial autocorrelation across all seasons, with the strongest correlation observed in winter. The local spatial autocorrelation analysis identified significant clustering in hotspots, highlighting spatial interdependencies critical for marketing and infrastructure planning. This study uses big data analytics to advance the understanding of tourist behavior, offering critical insights for crafting sustainable tourism strategies and policies that align with demographic, temporal, and seasonal dynamics. The findings not only contribute to current empirical research but could also aid local governments and stakeholders in optimizing tourism management and enhancing visitor experiences on Jeju Island.

In the face of growing concerns about urban problems, smart cities have emerged as a promising solution to address the challenges, for future sustainable societies in cities. Since the early 2000s, 67 local governments in Korea have been participating in smart city projects, as of 2019. The Sejong 5-1 Living Area smart city was selected as one of two pilot national demonstration smart cities. The main objectives of this study are to introduce the Sejong 5-1 Living Area smart city project that is currently in the planning stage, present travel and mode preferences focusing on external trips in a smart city context to be built, and analyze a mode choice model according to the socioeconomic characteristics of individual travelers. One of the distinguishing features of the Sejong smart city is its transportation design concept of designating a sharing car-only district within the city to limit private vehicle ownership to about one-third of residents, while bus rapid transit (BRT) plays a central role in mobility for external trips among four transport modes including private cars, BRT, carsharing, and ridesharing. This study was analyzed using the stated preference survey data under hypothetical conditions by reflecting the unique characteristics of the Sejong smart city transportation policy. Approximately two-thirds of respondents in the survey preferred to spend less than 1.25 USD, traveling less than 35 min on BRT trips. On the basis of the survey data, we developed a mixed logit mode choice model and found the overall model estimates to be statistically significant and reasonable. All people-specific variables examined in this study were associated with mode choices for external commuting trips, including age, income, household size, major mode, driving ability, and presence of preschoolers.

Activity spaces are places in which people participate in an activity during a certain time period, serving as the starting or ending point of a trip. Previous studies on trip generation or attraction models have been limited to aggregated spatial and temporal scales. Extending recent research on the application of mobile phone data to transportation models and particularly addressing the above-mentioned limitations, this study investigates temporal and spatial characteristics of hourly activity space attractiveness by activity type at finer spatial (e.g. census block) and temporal (e.g. hourly) resolutions. We construct hourly activity space attraction models in the daytime for four activity types, including work, private education, shopping, and recreation in Gangnam, Korea. The models are log-transformed or square-root-transformed multiple regression models. We also test the spatial transferability of the Gangnam model to the area of Seocho, Korea.