Travel Behaviour and Society

Papers
(The H4-Index of Travel Behaviour and Society is 35. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-07-01 to 2024-07-01.)
ArticleCitations
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models202
Dockless E-scooter usage patterns and urban built Environments: A comparison study of Austin, TX, and Minneapolis, MN168
Commute satisfaction, neighborhood satisfaction, and housing satisfaction as predictors of subjective well-being and indicators of urban livability96
COVID-19, activity and mobility patterns in Bogotá. Are we ready for a ‘15-minute city’?91
Ready for Mobility as a Service? Insights from stakeholders and end-users73
Future implementation of mobility as a service (MaaS): Results of an international Delphi study66
Why travelers trust and accept self-driving cars: An empirical study65
Who are the potential users of shared e-scooters? An examination of socio-demographic, attitudinal and environmental factors59
A big data approach to understanding pedestrian route choice preferences: Evidence from San Francisco57
The role of habit and the built environment in the willingness to commute by bicycle57
Relationships of the multi-scale built environment with active commuting, body mass index, and life satisfaction in China: A GSEM-based analysis55
Spatial accessibility assessment of COVID-19 patients to healthcare facilities: A case study of Florida54
Modelling work- and non-work-based trip patterns during transition to lockdown period of COVID-19 pandemic in India51
Creation of mobility packages based on the MaaS concept50
Who is interested in a crowdsourced last mile? A segmentation of attitudinal profiles48
Predicting the travel mode choice with interpretable machine learning techniques: A comparative study46
Investigating heterogeneity in preferences for Mobility-as-a-Service plans through a latent class choice model45
Understanding electric bike riders’ intention to violate traffic rules and accident proneness in China45
Long commutes and transport inequity in China’s growing megacity: New evidence from Beijing using mobile phone data45
Perception of the built environment and walking in pericentral neighbourhoods in Santiago, Chile45
Travel behaviour changes under Work-from-home (WFH) arrangements during COVID-1943
Young people's perceived service quality and environmental performance of hybrid electric bus service43
Factors influencing consumer acceptance of vehicle-to-grid by electric vehicle drivers in the Netherlands43
Public transit travel choice in the post COVID-19 pandemic era: An application of the extended Theory of Planned behavior42
A comparative analysis of the users of private cars and public transportation for intermodal options under Mobility-as-a-Service in Seoul40
Understanding user practices in mobility service systems: Results from studying large scale corporate MaaS in practice39
Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction39
Examining public acceptance of autonomous mobility38
Factors influencing user behaviour in micromobility sharing systems: A systematic literature review and research directions37
Determinants of children’s active travel to school: A case study in Hong Kong37
The influence of travel attitudes on perceived walking accessibility and walking behaviour37
Public transport users versus private vehicle users: Differences about quality of service, satisfaction and attitudes toward public transport in Madrid (Spain)37
A framework with efficient extraction and analysis of Twitter data for evaluating public opinions on transportation services36
A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study35
How the built environment promotes public transportation in Wuhan: A multiscale geographically weighted regression analysis35
Are travellers willing to adopt MaaS? Exploring attitudinal and personality factors in the case of Madrid, Spain35
Trends in air travel inequality in the UK: From the few to the many?35
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