Travel Behaviour and Society出行行为与社会
Travel Behaviour and Society(英文缩写 TRAVEL BEHAV SOC),ISSN 2214-367X,eISSN 2214-3688,中文译名:出行行为与社会 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 5.850 | Q2 |
| 2022 | 5.200 | Q2 |
| 2023 | 5.100 | Q1 |
| 2024 | 5.700 | Q1 |
| 2025 | 5.800 | Q1 |
Travel Behaviour and Society 最新收录文献
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1. Assessing bicycle safety risks using emerging mobile sensing data.
PMID:日期:2025-01-01The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there's a need to reassess the risk factors associated with multiple bike lane users. Based on this, there's a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.
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2. Exploring the influences of personal attitudes on the intention of continuing online grocery shopping after the COVID-19 pandemic.
PMID:日期:2023-10-01The unprecedented COVID-19 pandemic has brought drastic changes in our daily activities. One of these essential activities is grocery shopping. In compliance with the recommended social distancing standards, many people have switched to online grocery shopping or curbside pickup to minimize possible contagion. Although the shift to online grocery shopping is substantial, it is not clear whether this change would last in the long term. This study examines the attributes and underlying attitudes that may influence individuals' future decisions on online grocery shopping. An online survey was conducted in May 2020 in South Florida to collect data for this study. The survey contained a comprehensive set of questions related to respondents' sociodemographic attributes, shopping and trip patterns, technology use, as well as attitudes toward telecommuting and online shopping. A structural equation model (SEM) was applied to examine the intervening effects of observed as well as latent attitude variables on the likelihood of online grocery shopping after the outbreak. The results indicated that those with more experience in using online grocery shopping platforms were more likely to continue purchasing their groceries online. Individuals with positive attitudes toward technology and online grocery shopping in terms of convenience, efficiency, usefulness, and easiness were more likely to adopt online grocery shopping in the future. On the other hand, pro- driving individuals were less likely to substitute online grocery shopping for in-store shopping. The results suggested that attitudinal factors could have substantial impacts on the propensity toward online grocery shopping.
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3. Applying an interpretable machine learning framework to study mobility inequity in the recovery phase of COVID-19 pandemic.
PMID:日期:2023-10-01The COVID-19 pandemic is a public health crisis that also fuels the pervasive social inequity in the United States. Existing studies have extensively analyzed the inequity issues on mobility across different demographic groups during the lockdown phase. However, it is unclear whether the mobility inequity is perennial and will continue into the mobility recovery phase. This study utilizes ride-hailing data from Jan 1st, 2019, to Mar 31st, 2022, in Chicago to analyze the impact of various factors, such as demographic, land use, and transit connectivity, on mobility inequity in the different recovery phases. Instead of commonly used statistical methods, this study leverages advanced time-series clustering and an interpretable machine learning algorithm. The result demonstrates that inequity still exists in the mobility recovery phase of the COVID-19 pandemic, and the degree of mobility inequity in different recovery phases is varied. Furthermore, mobility inequity is more likely to exist in the census tract with more families without children, lower health insurance coverage, inflexible workstyle, more African Americans, higher poverty rate, fewer commercial land use, and higher Gini index. This study aims to further the understanding of the social inequity issue during the mobility recovery phase of the COVID-19 pandemic and help governments propose proper policies to tackle the unequal impact of the pandemic.
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4. The impact of COVID-19 lockdown measures on gendered mobility patterns in France.
PMID:日期:2023-10-01The COVID-19 crisis has upset the way of life of our society. The objective of this study was to apprehend the consequences of public health policies on mobility through the lens of gender. The analyses are based on a representative sample of 3000 people living in France. Travel behaviour was quantified using three mobility indicators (number of daily trips, daily distance travelled and daily travel time) that we regressed on individual and contextual explanatory variables. Two periods were studied: lockdown (March 17, 2020 until May 11, 2020), and post-lockdown (a curfew period: January-February 2021). For the lockdown period, our results show: (i) a statistically significant gender difference for the three mobility indicators. On average, women made 1.19 daily trips versus 1.46 for men, travelled 12 km whereas versus 17 km for men and spent less time on travel (23 min) than men (30 min); (ii) the degree of mobility was particularly sensitive to access to a car, according to a gender difference. For the post-lockdown period, our results reveal that: (i) women were more likely than men to make a higher number of daily trips (OR = 1.10, 95% CI = [1.04-1.17]); (ii) having only one or no car in the household impacted the mobility of women during the post-lockdown period; (iii) women regained some mobility but without reaching the pre-lockdown level. A better understanding of the factors influencing mobility behaviour, in lockdown and curfew periods, can provide some pathways to improve transport planning and help public authorities while tackling gender inequalites.
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5. Understanding changing public transit travel patterns of urban visitors during COVID-19: A multi-stage study.
PMID:日期:2023-07-01COVID-19 has caused huge disruptions to urban travel and mobility. As a critical transportation mode in cities, public transit was hit hardest. In this study, we analyze public transit usage of urban visitors with a nearly two-year smart card dataset collected in Jeju, South Korea - a major tourism city in the Asia Pacific. The dataset captures transit usage behavior of millions of domestic visitors who traveled to Jeju between January 1, 2019 and September 30, 2020. By identifying a few key pandemic stages based on COVID-19 timeline, we employ ridge regression models to investigate the impact of pandemic severity on transit ridership. We then derive a set of mobility indicators - from perspectives of trip frequency, spatial diversity, and travel range - to quantify how individual visitors used the transit system during their stay in Jeju. By further employing time series decomposition, we extract the trend component for each mobility indicator to study long-term dynamics of visitors' mobility behavior. According to the regression analysis, the pandemic had a dampening effect on public transit ridership. The overall ridership was jointly affected by national and local pandemic situations. The time series decomposition result reveals a long-term decay of individual transit usage, hinting that visitors in Jeju tended to use the transit system more conservatively as the pandemic endured. The study provides critical insights into urban visitors' transit usage behavior during the pandemic and sheds light on how to restore tourism, public transit usage, and overall urban vibrancy with some policy suggestions.
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6. Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data.
PMID:日期:2023-07-01The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.
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7. Nowhere to go - Effects on elderly's travel during Covid-19.
PMID:日期:2023-07-01The COVID-19 pandemic has presented numerous, significant challenges for elderly in their daily life. In order to reach a deeper understanding of the feelings and thoughts of the elderly related to their possibilities to travel and engage in activities during the pandemic, this study takes a qualitative approach to exploring the views of the elderly themselves. The study focuses on experiences during the COVID-19 pandemic. A number of in-depth semi-structured interviews with elderly aged 70 and above, were conducted in June 2020. Applied Thematic Analysis (ATA) was applied, as a first stage, to investigate meaningful segments of data. In a second stage these identified segments were combined into a number of themes. This study reports the outcome of the ATA analysis. More specifically we report experiences, motivations and barriers for travel and activity participation, and discuss how these relate to the health and well-being of elderly, and vice versa. These findings highlight the strong need to develop a transport system that to a higher extent addresses the physical as well as the mental health of old people, with a particular focus on facilitating social interactions.
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8. Impacts of the COVID-19 pandemic on the profile and preferences of urban mobility in Brazil: Challenges and opportunities.
PMID:日期:2023-04-01Daily commuting characteristics were highly affected by the COVID-19 pandemic, since restriction of the movement of people was one of the main preventive measures adopted. Understanding of the effects that the pandemic had on mobility is essential to help in mitigating the problems arising from this crisis, while also providing an opportunity for the implementation of sustainable policies in the post-pandemic period. Therefore, the aim of this study was to identify the impacts of the pandemic on the profile of travel behavior and mobility preferences in Brazil, using a case study of cities located in the state of Rio Grande do Sul. The data obtained from an online survey were modeled using exploratory factor analysis, resulting in the extraction of 15 main factors that explain behavioral changes in mobility due to the effects of the pandemic, as well as future perspectives. In the pandemic period, the use of private vehicles grew as the main mode of transport to the principal activity. Conversely, the use of public transport decreased drastically, due to compulsory measures taken by the health authorities to prevent the spread of the new virus. There was also greater receptivity to the adoption of active mobility, especially the bicycle, although it is necessary to provide better conditions for use of this transport mode. The findings support the development of public policies to reduce urban mobility problems and to provide guidelines for sustainable planning in the post-pandemic period.
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9. In-person, pick up or delivery? Evolving patterns of household spending behavior through the early reopening phase of the COVID-19 pandemic.
PMID:日期:2023-04-01Consumer reactions to COVID-19 pandemic disruptions have been varied, including modifications in spending frequency, amount, product categories and delivery channels. This study analyzes spending data from a sample of 720 U.S. households during the start of deconfinement and early vaccine rollout to understand changes in spending and behavior one year into the pandemic. This paper finds that overall spending is similar to pre-pandemic levels, except for a 28% decline in prepared food spending. More educated and higher income households with children have shifted away from in-person spending, whereas politically conservative respondents are more likely to shop in-person and via pickup.
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10. Reactions of the public transport sector to the COVID-19 pandemic. Insights from Belgium.
PMID:日期:2023-04-01Throughout the COVID-19 pandemic, public transport has been one of the hardest hit transport modes, losing ridership due to fear of contagion. This can partially be explained by the lack of preparedness in the sector to a pandemic scenario, as only few cities had epidemic contingency plans for the transport sector. To anticipate disruptions caused by future crises, we look at the preparedness and the response to COVID-19 by the public transport sector in Belgium. We interview all public transport operators in Belgium and analyze the interviews through the disaster management framework. We also aim to distill the lessons that can be learned from the pandemic to increase resilience in future public transport planning. We find that no operator in Belgium had contingency plans ready for a pandemic scenario, but that other plans were deployed to adapt their offer to COVID-19 conditions. Although all operators lost a significant part of ridership, their offer was maintained throughout the crisis, albeit at a decreased level for some operators. The availability of reliable and real-time data is identified as an important learning by the operators, as well as the ability to identify a core response team in case of a crisis. COVID-19 was seen by the operators as a learning platform to face future crises and highlighted the need to increase reactivity through better preparedness and data availability. We recommend the structural use of foresight methods through for example scenario planning to increase the preparedness of operators in the case of future disruptions.