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IntroductionThe global Coronavirus (COVID-19) pandemic is having a great impact on all areas of the everyday life, including travel behaviour. Various measures that focus on restricting social contacts have been implemented in order to reduce the spread of the virus. Understanding how daily activities and travel behaviour change during such global crisis and the reasons behind is crucial for developing suitable strategies for similar future events and analysing potential mid- and long-term impacts.MethodsIn order to provide empirical insights into changes in travel behaviour during the first Coronavirus-related lockdown in 2020 for Germany, an online survey with a relative representative sample for the German population was conducted a week after the start of the nationwide contact ban. The data was analysed performing descriptive and inferential statistical analyses.Results and DiscussionThe results suggest in general an increase in car use and decrease in public transport use as well as more negative perception of public transport as a transport alternative during the pandemic. Regarding activity-related travel patterns, the findings show firstly, that the majority of people go less frequent shopping; simultaneously, an increase in online shopping can be seen and characteristics of this group were analysed. Secondly, half of the adult population still left their home for leisure or to run errands; young adults were more active than all other age groups. Thirdly, the majority of the working population still went to work; one out of four people worked in home-office. Lastly, potential implications for travel behaviour and activity patterns as well as policy measures are discussed.

This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighborhood was negatively correlated with E-scooter usage.

The emergence of ride-hailing, technology-enabled on-demand services such as Uber and Lyft, has arguably impacted the daily travel behavior of users. This study analyzes the travel behavior of ride-hailing users first from conventional person- and trip-based perspectives and then from an activity-based approach that uses tours and activity patterns as basic units of analysis. While tours by definition are more easily identified and classified, daily patterns theoretically better represent overall travel behavior but are simultaneously more difficult to explain. We thus consider basic descriptive analyses for tours and a more elaborate approach, Latent Class Analysis, to describe pattern behavior. The empirical results for tours using data from the 2017 National Household Travel Survey show that 76% of ride-hailing tours can be represented by five dominant tour types with non-work tours being the most frequent. The Latent Class model suggests that the ride-hailing users can be divided into four distinct classes, each with a representative activity-travel pattern defining ride-hailing usage. Class 1 was composed of younger, employed people who used ride-hailing to commute to work. Single, older individuals comprised Class 2 and used ride-hailing for midday maintenance activities. Class 3 represented younger, employed individuals who used ride-hailing for discretionary purposes in the evening. Last, Class 4 members used ride-hailing for mode change purposes. Since each identified class has different activity-travel patterns, they will show different responses to policy directives. The results can assist ride-hailing operators in addressing evolving travel needs as users respond to various policy constraints.

This study utilised the Swedish national travel survey covering a period of over 30 years. We investigated the long-term trends in activity-travel patterns of individuals in different life-cycle stages and generations using cohort analysis and a path model. The main findings are summarised as follows. The women, including mothers, in younger generations have become more active in out-of-home non-work activities and their trip chaining has become more complex, compared to their male counterparts. While men are still driving more than women, the gap is decreasing in the younger generations. The gender difference among teenagers in terms of out-of-home time use diminishes in younger generations. Teenagers of younger generations spend more of their leisure time inside their homes, possibly due to the rise of online activities and gaming and more time-consuming school trips, the latter attributed to changes in school choice policy. Older adults travel more, possibly due to better paratransit transport service, supported by better health services.

The primary objective of this study was to investigate the bikesharing travel patterns and trip purposes by combining smart card data and online point of interests (POIs). A large-scale smart card trip data was collected from the bikesharing system in New York City. The POIs surrounding each station were obtained from Google Places API. K-means clustering analysis was first applied to divide bikesharing stations into five types based on their surrounding POIs. The Latent Dirichlet Allocation (LDA) analysis was then conducted to discover the hidden bikesharing travel patterns and trip purposes using the identified station types and smart card data. The performance of the LDA models with and without POI data was compared to identify whether the POI data should be used. Finally, a practical application of the proposed methods in bikesharing planning and operation was discussed. The result of comparative analyses verified the importance of POI data in exploring bikesharing travel patterns and trip purposes. The results of LDA model showed that the most prevalent travel purpose in New York City is taking public bike for eating, followed by shopping and transferring to other public transit systems. In addition, the result also suggested that people living around the bikesharing stations are more likely to transfer to other commuting tools on the morning peak and ride for home after work. The proposed methods can be used to provide useful guidance and suggestions for transportation agency to develop strategies and regulation that aim at improving the operations of bikesharing systems.

Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.

In recent urban studies, understanding the flow patterns of urban residents has become crucial for effective transportation planning and business district design. Traditional data-driven approaches have provided insights but often lead to random and uninterpretable results due to their sole reliance on data features, lacking a deeper contextual and semantic analysis of the underlying patterns. To overcome these limitations, our work introduces a novel framework that fuses holographic knowledge embedding with graph deep learning to predict urban population travel patterns. This dual-driven approach of data and knowledge uniquely integrates traffic geographic information, vehicle trajectory data, and Points of Interest (POI) into a comprehensive urban traffic knowledge graph. Our method not only captures the spatial-temporal dependencies of big data traffic but also models the relationships between geographic, semantic POI information, and urban travel behaviors. The knowledge graph is then processed through a graph deep learning model, enhancing the embedding features and enabling sophisticated link prediction. Compared with conventional data-driven methods, our approach demonstrates significant advancements in harnessing semantic information, leading to more accurate and interpretable predictions of travel patterns. Experimental validation on real-world datasets confirms the effectiveness of our method in capturing complex urban dynamics.

The spatial mismatch between jobs and housing in cities creates long daily travels that exacerbate climate change, air pollution, and traffic congestion. Yet, not enough research on occupational differences has been done. This study first applies the Hidden Markov Mixture Model (H3M) to model travel patterns for different occupation groups in Hong Kong. Then, the Variational Bayesian Hierarchical EM algorithm is used to identify common lifestyle clusters. Next, a binary logistic regression is developed to examine whether the lifestyle clusters can be explained by jobs-housing balance. This study is among the first to consider travel patterns as a Markov process and apply H3M to examine jobs-housing balance by fine-grain occupation group. The method is transferable and universally applicable; and the results provide occupation-specific insights on jobs-housing balance in an Asian context. The research findings suggest that different occupation groups have different travel patterns in Hong Kong. Two lifestyle clusters, “balanced and compact activity space” and “work-oriented and extensive travels”, are unveiled. Notably, the latter is associated a lower level of jobs-housing balance. Some occupations in the quaternary industry (“information and communications”, “profession, science and technology”, “real estate”, and “finance and insurance”) are having more serious jobs-housing imbalance. The paper concludes with a discussion on improving the occupation-specific jobs-housing balance in accordance with Hong Kong’s future development goals.

In reducing greenhouse gas (GHG) emissions, there is a recognition triggered by the pandemic of the role that walking and cycling (active travel) can make to substitute motorized travel, particularly on short trips. However, there is a lack of evidence at the micro level on the realistic, empirically derived, potential of these options. Here, we used reliable tracing data to examine the potential of these mitigation options for reducing GHG emissions in Vietnam. Apart from similar categories of travel purposes as in other studies, we decided to categorize “visit relatives” and “eating out” as two more separate categories of travel purposes in Vietnamese case, which together accounts for nearly 16% of total trips. We discovered that 65% of all motorcycle trips in this case study were less than 3 miles in duration, therefore active travel was able to create a significant impact on GHG emissions from personal travel. Active travel can replace 62% of short motorcycle trips if considering travel patterns and constraints while saving 18% of GHG emissions that would have come from motorized transport. If active travel can further replace all shopping trips normally done by motorcycles, in total being equivalent to 84% of short trips, 22% of GHG emissions from motorcycles can be reduced. It should be noticed that active travels have time cost implications, impacting economy at both household and city levels, but from a comprehensive “co-benefit” standpoint, this transformation could act as a catalyst for addressing traffic congestion, air pollution, and even community health and well-being in urban areas.