Travel Behaviour and Society最新文献

{"title":"Vehicle automation, onboard environment, and in-vehicle time use: Findings from a stated choice experiment","authors":"Sailesh Acharya","doi":"10.1016/j.tbs.2025.101076","DOIUrl":"10.1016/j.tbs.2025.101076","url":null,"abstract":"<div><div>This study examines the impact of vehicle automation and onboard activities on travel mode preferences based on a stated choice experiment conducted among 696 visitors to US national parks. Several multinomial and mixed logit models were estimated to analyze preferences for human-driven vehicles (HV), autonomous vehicles (AV), and AVs with work and leisure interiors (AV-WL), and to calculate the associated value of travel time (VOTT). The estimated VOTT values for HV, AV, and AV-WL were $34.80, $31.10, and $29.90 per hour, respectively. The reduction in VOTT from HV to AV and further from AV to AV-WL indicates that vehicle automation and onboard environments have a tangible monetary impact on travelers’ perceived value of time. While in-vehicle activities did not significantly contribute to VOTT for HV, entertainment activities had a measurable impact on VOTT for AV and AV-WL. Although the results indicate that the influence of in-vehicle activities and onboard environments on AV adoption and associated VOTT is less substantial than expected, they highlight that vehicle automation, interior designs, and the propensity for in-vehicle activities and their usefulness are critical factors influencing mode choice decisions in the AV era. Furthermore, these findings suggest that a shift in activity travel patterns due to automation is unlikely unless suitable vehicle interiors are designed to support work and leisure activities. Thus, from a time-use perspective, vehicle automation may exacerbate the issue of larger car sizes, increasing energy consumption and space requirements as automated vehicles with favorable and more spacious interiors become more desirable.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101076"},"PeriodicalIF":5.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Street matters: Linking perceived street environment to older adults’ bike-sharing","authors":"Tanhua Jin ,&nbsp;Xiaobing Wei ,&nbsp;Long Cheng ,&nbsp;Kailai Wang ,&nbsp;Yanan Xin ,&nbsp;Frank Witlox","doi":"10.1016/j.tbs.2025.101071","DOIUrl":"10.1016/j.tbs.2025.101071","url":null,"abstract":"<div><div>Shared micromobility has established its role as a viable solution for sustainable transportation worldwide. Despite the widespread discourse on bike-sharing, there remains a paucity of research addressing its utilization among older adults (aged 65 and over). To this end, this research delves into the analysis of docked bike-sharing trip records in Chicago, aiming to understand the impact of land use and perceived street environment (derived from Google Street View Images) on the utilization of bike-sharing services among older adults at both the station and route levels. This study adopts the Extreme Gradient Boosting (XGBoost) method and interprets the results using Shapley Additive explanations (SHAP). Results show that older adults have specific preferences for different land use and perceived street environments when using shared bikes, differing notably from the general user population. Regarding land use effects, older adults are more likely to use shared bikes in areas with high mixed land use and more green spaces. Areas with more cycling lane density also increase older adults’ likelihood to cycle more. For perceived street environments, older adults are prone to streets with high enclosure and low sky openness levels. This research also finds that the route-level perceived street environment has more pronounced marginal effects in comparison to the station-level counterparts. Our findings can provide evidence-based guidance to transportation planners to develop age-friendly transportation systems, thus alleviating the potential inequalities in access to bike-sharing services.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101071"},"PeriodicalIF":5.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting urban mobility patterns with a LightGBM-enhanced gravity model: Insights from the Wuhan metropolitan area","authors":"Zhenyu Zhang ,&nbsp;Mengzhao Yang ,&nbsp;Liyuan Zhao ,&nbsp;Zhi-Chun Li","doi":"10.1016/j.tbs.2025.101070","DOIUrl":"10.1016/j.tbs.2025.101070","url":null,"abstract":"<div><div>Accurately quantifying urban human mobility is crucial for tackling challenges in traffic engineering, urban planning and public health. Traditional static gravity models (GModel) often fail to address spatial heterogeneity and non-linear relationships in mobility flows, particularly in complex urban regions with new towns and metropolitan areas. This study investigates mobility flows in the Wuhan Metropolitan Area by introducing an Intelligent Gravity Model (IGModel) that integrates the theoretical insights of gravity models with the non-linear predictive capacity of LightGBM. The IGModel extends the gravity model by incorporating built environment and geometric variables while leveraging machine learning to enhance flow predictions. Through this hybrid approach, the IGModel achieves robust predictive performance (R-squared = 0.97) and provides interpretable insights using SHAP (Shapley Additive Explanations) analysis. The results demonstrate the complementary strengths of mechanism-driven and data-driven approaches, with the IGModel outperforming dynamic gravity models (DGModel) and offering actionable insights for urban planning and transportation management.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101070"},"PeriodicalIF":5.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Getting around on foot: Older adults’ walking experiences and perspectives on neighbourhood walkability across Canada","authors":"Megan James ,&nbsp;Meredith Alousi-Jones ,&nbsp;Aryana Soliz ,&nbsp;Ahmed El-Geneidy","doi":"10.1016/j.tbs.2025.101074","DOIUrl":"10.1016/j.tbs.2025.101074","url":null,"abstract":"<div><div>Older adults’ living environments are instrumental in making walking part of their daily lives, as we strive to promote healthy aging. Objective measures, such as WalkScore®, and subjective measures of walkability provide means to grasp the factors that enable or hinder frequent and enjoyable walking. However, there is limited consensus on what factors contribute to mismatch between perceptions of walkability and objective built environment measures, particularly among older adults. We interviewed fifty-eight older adults (65 +) from six Canadian cities to uncover the relationship between their perceived neighbourhood walkability and objective built environment measures. We segmented our interviewee sample into four categories based on their residential WalkScore® and perceptions of neighbourhood walkability. Our thematic analysis provides insight into strategies older adults use to respond to barriers to walking in their environment and walking facilitators they experience in their neighbourhoods. The findings can be of interest to practitioners and decision-makers as they seek to improve walking environments for aging populations, ultimately contributing to older adults’ long-term health and well-being.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101074"},"PeriodicalIF":5.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding factors influencing user retention in shared e-scooter schemes: A comparative study of the UK/EU and the US","authors":"Yuanxuan Yang ,&nbsp;Charalampos Saridakis ,&nbsp;Zia Wadud ,&nbsp;Alimurtaza Kothawala ,&nbsp;Aurojeet Jena ,&nbsp;Basar Ozbilen ,&nbsp;Kailai Wang ,&nbsp;Giovanni Circella ,&nbsp;Sebastian Castellanos ,&nbsp;Susan Grant-Muller","doi":"10.1016/j.tbs.2025.101067","DOIUrl":"10.1016/j.tbs.2025.101067","url":null,"abstract":"<div><div>Shared e-scooters are increasingly recognised as a sustainable travel mode for tackling urban transportation challenges, including congestion and air pollution. Their early adoption in U.S. cities, followed by expansion across Europe and ongoing trials in the UK, underscores the need to understand key factors for the scheme’s success and development, particularly regarding the acceptance and retention of existing users. This study adopts an explainable machine learning approach to predict and understand the likelihood of continued shared e-scooter usage among current users. It provides insights into the important factors influencing user retention, while also comparing the UK/EU and US contexts. The developed models exhibit high predictive accuracy and highlight the perceived utility and user-friendliness of e-scooters as key determinants influencing users’ willingness for future use. The research sheds light on psychographic characteristics, emphasising the impact of green awareness and individual’s values on users’ retention, and how their importance may vary between the UK/EU and US users. The findings are useful for guiding future strategic development of shared e-scooter services in different geographic contexts.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101067"},"PeriodicalIF":5.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Realtime GTFS to generate easy-to-use transit accessibility measures under travel time uncertainty","authors":"Reyhane Javanmard ,&nbsp;Luyu Liu ,&nbsp;Jed A. Long ,&nbsp;Jinhyung Lee","doi":"10.1016/j.tbs.2025.101054","DOIUrl":"10.1016/j.tbs.2025.101054","url":null,"abstract":"<div><div>Previous studies on measuring transit accessibility under travel time uncertainty often introduced complex measures based on non-standard data formats, hindering reproducibility and replicability in research and planning. To address this, we present a practical framework that leverages a standardized format for real-time transit data: Realtime General Transit Feed Specification (GTFS), to generate easy-to-use transit accessibility measures under travel time uncertainty. This framework first produces two datasets by correcting Scheduled GTFS data using Realtime GTFS information: Realtime P50 GTFS and Realtime P85 GTFS, which are used to compute two accessibility measures<em>: median-corrected accessibility</em> (using Realtime P50 GTFS) and <em>dispersion-corrected accessibility</em> (using Realtime P85 GTFS). These accessibility measures are applied in Columbus, Ohio, USA for an empirical study examining how overlooking travel time uncertainty issues can distort the analysis results of healthcare accessibility, inequality, and new transit project evaluation. Results indicate that <em>scheduled accessibility</em> (using Scheduled GTFS data) which overlooks travel time uncertainty overestimates healthcare accessibility by approximately 10.97 %. Moreover, this oversight fails to capture the benefits of the new transit service in improving accessibility and reducing inequality. Furthermore, although findings consistently suggest that lower-income neighbourhoods experience greater gains in healthcare accessibility compared to wealthier counterparts, our analysis unveils statistically significant differences when using the scheduled and dispersion-corrected accessibility measures. These findings underscore the importance of incorporating travel time uncertainty into public transit planning and evaluation. Our framework allows transit authorities and researchers to accurately measure accessibility and evaluate projects under travel time uncertainty using a standardized data format.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101054"},"PeriodicalIF":5.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ride-hailing vs. public transport: Comparing travel time perceptions using revealed preference data from Washington DC","authors":"Menno Yap,&nbsp;Oded Cats","doi":"10.1016/j.tbs.2025.101069","DOIUrl":"10.1016/j.tbs.2025.101069","url":null,"abstract":"<div><div>Ride-hailing has become an important part of the urban mobility landscape. The main contribution of this study is to estimate how travellers perceive time when using ride-hailing compared to using conventional public transport, to better understand ride-hailing mode choice. We combine two unique datasets containing actual, individual passenger behaviour for the Washington DC area from October 2018: a large set of almost 250,000 individual ride-hailing trips made using Uber, and more than 326,000 public transport trips obtained from automated ticketing data. Contrary to previous studies our model estimations rely on over half a million directly observed passenger choices between ride-hailing and public transport, based on which we estimate a discrete choice model to infer travel time perceptions for both modes using a binomial logit model. Our results show that on average ride-hailing in-vehicle time is perceived 35% less negative than public transport in-vehicle time. We also found that waiting time for ride-hailing is valued 1.3 times more negative than ride-hailing in-vehicle time, which is about 20% less negative than the ratio between waiting and in-vehicle time found for public transport. Our study enables a more accurate modelling of ride-hailing by using mode-specific travel time coefficients derived from large-scale empirical data, which can improve the accuracy of modelling outputs and thus improve decision-making processes.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101069"},"PeriodicalIF":5.1,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What street view imagery features favour driving? A copula model for driver distraction and driving performance","authors":"Shile Zhang ,&nbsp;N.N. Sze ,&nbsp;Mohamed Abdel-Aty","doi":"10.1016/j.tbs.2025.101068","DOIUrl":"10.1016/j.tbs.2025.101068","url":null,"abstract":"<div><div>Urban landscape plays a crucial role in reshaping the activity and mobility pattern of citizens. Studies have explored the relationships between the built environment, socio-economic, transport infrastructure, travel behaviour, and quality of life at different spatial scales. However, associations between the built environment, driver distraction, and driving performance at the micro-level are less studied. In this study, influences of different visual objects from drivers’ view and other possible factors on driver distraction and speed variation are investigated. Based on the street view imagery and image segmentation technique, proportions of visible objects including vegetation and road furniture within driver perspective can be estimated. Furthermore, vehicle kinematics in terms of longitudinal speed, longitudinal acceleration, and lateral acceleration can be measured from vehicle trajectory data. The Gaussian distributed copula model is used to jointly model the ratio of driver distraction and speed standard deviation. Results indicate that proportions of road, sky, and buildings in the drivers’ view significantly affect driver distraction ratio. In addition, speed standard deviation is associated with driver distraction ratio, proportions of sky and buildings, vehicle longitudinal and lateral acceleration, and driver age. Findings should shed light on enhancing urban design and planning by considering the effects of built environment attributes and drivers’ visual environment.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101068"},"PeriodicalIF":5.1,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The influences of built environment on ultra-peak for urban rail transit station passenger flows based on lasso-multiscale geographically weighted regression","authors":"Xing Zhao ,&nbsp;Yangxin Li ,&nbsp;Jinxin Sun ,&nbsp;Liang Li ,&nbsp;Gang Ren","doi":"10.1016/j.tbs.2025.101063","DOIUrl":"10.1016/j.tbs.2025.101063","url":null,"abstract":"<div><div>Due to the significant spatial–temporal deviations between station-level and network-wide ultra-peak passenger flows, this study focuses on identifying determinants of ultra-peak time and volume at urban rail transit stations on working days. Five types of influencing factors are analyzed: land-use, population, network attributes, intermodal connections, and station characteristics. To achieve model simplicity and predictive accuracy, we employ Lasso regularization for variable selection, followed by multiscale geographically weighted regression (MGWR) models. Four Lasso-MGWR (L-MGWR) models are developed to quantify: (1) ultra-peak boarding time (UPTB), (2) ultra-peak alighting time (UPTA), (3) ultra-peak boarding volume (UPVB), (4) ultra-peak alighting volume (UPVA). Using AFC card data from Hangzhou, China (January 1-26, 2019), the results demonstrate: (1) Model performance: L-MGWR models significantly outperform conventional OLS and GWR models, achieving 12–27% higher R² values and 17–31% lower RSS. (2) Variable influence patterns: UPTB is primarily driven by Transportation land-use (TLU), Residential land-use (RLU), Working land-use (WLU), Science, education, and cultural land-use (PLU), Distance to downtown (DD) and Closeness centrality (CC); UPTA is influenced by TLU, RLU, WLU, Healthcare land-use (HLU), Primary and secondary schools (SLU), DD and CC; UPVB is determined by RLU, WLU, HLU, Bus stop density (BS), Transfer station (TF) and DD; UPVA is affected by TLU, RLU, WLU, SLU and CC. (3) Spatial heterogeneity: These influencing factors all exhibit significant spatial heterogeneity, such as DD exhibits contrasting effects: positive in urban core areas and negative in suburban regions for UPVB. (4) Land-use interaction effects; mixed-use development integrating transportation, healthcare, and education facilities around stations can mitigates the ultra-peak passenger concentration to some extent. These findings provide actionable insights for ultra-peak prediction, station capacity planning, and transportation management optimization for new stations.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101063"},"PeriodicalIF":5.1,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of airline itinerary attributes and air passenger characteristics on air itinerary choice: An exploratory analysis using revealed preference data","authors":"Mustansir Farooq,&nbsp;M. Manoj,&nbsp;K. Ramachandra Rao","doi":"10.1016/j.tbs.2025.101064","DOIUrl":"10.1016/j.tbs.2025.101064","url":null,"abstract":"<div><div>Indian civil aviation being a service-oriented industry demands prioritizing consumer interests. To accurately gauge consumer interests, it is crucial to consider the behavioural characteristics of passengers. This study provides a comprehensive overview of the passenger characteristics and itinerary attributes in the selection of itinerary among various itinerary types available in the Indian domestic aviation market. This study uses a revealed preference-based questionnaire survey to collect the socio-economic and itinerary specific attributes of passengers to study airline itinerary choice. The results from exploratory data analysis and multinomial logistic regression reveal that there is a significant influence of trip purpose (business, leisure and visiting friends and relatives), booking method, time of day, route category etc., on airline itinerary choice of passengers. We found that flyers residing in the South Zone opt mostly for direct and connected itineraries, North and East Zone flyers prefer nonstop itineraries and Central Zone flyers prefer more connected itineraries among the available itinerary types. In addition, the effects of route category indicated that flyers traveling on Category 1 and 2 routes (Appendix 1) are more likely to opt for nonstop and direct itineraries, while route Category 2A flyers prefer nonstop itineraries and flyers traveling on Category 3 routes are less likely to choose a nonstop itinerary. Positive effects of travel planning, seating comfort, baggage, time flexibility and airline itinerary choice are also noted. The insights provided by the study can be utilized by airlines and airport operators for efficient planning of the airline itineraries in the country.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"41 ","pages":"Article 101064"},"PeriodicalIF":5.1,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144067190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}