Pub Date : 2025-11-18DOI: 10.1007/s11116-025-10697-4
K. W. Axhausen
{"title":"Editorial note on this collection of “data papers”","authors":"K. W. Axhausen","doi":"10.1007/s11116-025-10697-4","DOIUrl":"https://doi.org/10.1007/s11116-025-10697-4","url":null,"abstract":"","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"7 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145536722","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}
Currently, Japan is entering a phase of infrastructure improvement that requires repair of highways and bridges. However, large-scale renewal work that requires long-term road closure is rare. Therefore, this study analyzes the construction activities on the Hanshin Expressway, which involve road closures, to understand their impact on traffic conditions. Understanding the actual situation is useful for improving prediction models and calibrating simulations. This study focused on vehicle-trajectory data, where the ease of data collection, quantity, and accuracy have improved in recent years. The use of vehicle-trajectory data enables the changing behavior of each vehicle to be tracked when road networks are disrupted. This paper presents a method for detecting the impact of roadworks on vehicle trajectories by comparing normal conditions, lane restrictions, and road closures. Specifically, we proposed a special origin and destination (OD) identification procedure and a route-choice variety index corresponding to changes in the set of selectable routes based on road closures. Subsequently, this was used to identify the characteristics of susceptible ODs based on the traffic flow, travel time, and route choice. The results showed that detours were extensive and spread over many routes during OD trips sandwiched between construction sections. The different levels and types of impact caused by the characteristics of ODs could be useful for traffic management in upcoming renewal projects.
{"title":"Vehicle trajectory changes on a closed road network due to major renewal works","authors":"Hiroe Ando, Yasuo Asakura, Takuya Maruyama, Shinji Nakagawa","doi":"10.1007/s11116-025-10698-3","DOIUrl":"https://doi.org/10.1007/s11116-025-10698-3","url":null,"abstract":"Currently, Japan is entering a phase of infrastructure improvement that requires repair of highways and bridges. However, large-scale renewal work that requires long-term road closure is rare. Therefore, this study analyzes the construction activities on the Hanshin Expressway, which involve road closures, to understand their impact on traffic conditions. Understanding the actual situation is useful for improving prediction models and calibrating simulations. This study focused on vehicle-trajectory data, where the ease of data collection, quantity, and accuracy have improved in recent years. The use of vehicle-trajectory data enables the changing behavior of each vehicle to be tracked when road networks are disrupted. This paper presents a method for detecting the impact of roadworks on vehicle trajectories by comparing normal conditions, lane restrictions, and road closures. Specifically, we proposed a special origin and destination (OD) identification procedure and a route-choice variety index corresponding to changes in the set of selectable routes based on road closures. Subsequently, this was used to identify the characteristics of susceptible ODs based on the traffic flow, travel time, and route choice. The results showed that detours were extensive and spread over many routes during OD trips sandwiched between construction sections. The different levels and types of impact caused by the characteristics of ODs could be useful for traffic management in upcoming renewal projects.","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"19 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509377","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}
Pub Date : 2025-11-14DOI: 10.1007/s11116-025-10692-9
Ziang Yao, Jonas Fahlbusch, Jan-Dirk Schmöcker, Dong Zhang
Since 2015, free-floating bicycle schemes have been flourishing worldwide due to their easy booking and mobile payment systems. Despite numerous publications, there remains a significant gap in understanding how app users select their bikes and how spatial barriers, such as intersections or temporary availability uncertainty (during peak times), influence their choices. In order to explore the factors affecting users’ preferences while using the smartphone app, an online survey was conducted in China in 2022. The study evaluates traveller preferences for bicycle usage based on visual stated preference scenarios in which screenshots with potential bike pick-up locations were presented to the respondents. Mixed logit and nested multinomial logistic regression were applied to 9512 valid observations from 1189 respondents. The findings reveal that walking and cycling distance, reservation time, cost, number of intersections, and risk are estimated to have a significant influence on which bicycle is chosen or whether walking is preferred. All parameters have the expected sign, though we observe significant heterogeneity among users. The nested structure furthermoreprovides evidence that travelers are more likely to change their bicycle choice than their reservation status. For example, extending the time a bike is reservable by 5 min can be as valuable as reducing the walking access time by 50–100 m or increasing the fare by 1 CNY. Our results further highlight the importance of detailed locational decisions where shared bicycles are placed. If the operator is able to place bicycles in locations where many travelers do not have to cross intersections, this can offset price increases.
{"title":"How do app users select a free-floating bicycle? A stated preference survey investigating choice behaviour using generated screenshots","authors":"Ziang Yao, Jonas Fahlbusch, Jan-Dirk Schmöcker, Dong Zhang","doi":"10.1007/s11116-025-10692-9","DOIUrl":"https://doi.org/10.1007/s11116-025-10692-9","url":null,"abstract":"Since 2015, free-floating bicycle schemes have been flourishing worldwide due to their easy booking and mobile payment systems. Despite numerous publications, there remains a significant gap in understanding how app users select their bikes and how spatial barriers, such as intersections or temporary availability uncertainty (during peak times), influence their choices. In order to explore the factors affecting users’ preferences while using the smartphone app, an online survey was conducted in China in 2022. The study evaluates traveller preferences for bicycle usage based on visual stated preference scenarios in which screenshots with potential bike pick-up locations were presented to the respondents. Mixed logit and nested multinomial logistic regression were applied to 9512 valid observations from 1189 respondents. The findings reveal that walking and cycling distance, reservation time, cost, number of intersections, and risk are estimated to have a significant influence on which bicycle is chosen or whether walking is preferred. All parameters have the expected sign, though we observe significant heterogeneity among users. The nested structure furthermoreprovides evidence that travelers are more likely to change their bicycle choice than their reservation status. For example, extending the time a bike is reservable by 5 min can be as valuable as reducing the walking access time by 50–100 <jats:italic>m</jats:italic> or increasing the fare by 1 CNY. Our results further highlight the importance of detailed locational decisions where shared bicycles are placed. If the operator is able to place bicycles in locations where many travelers do not have to cross intersections, this can offset price increases.","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"39 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509378","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}
Pub Date : 2025-11-14DOI: 10.1007/s11116-025-10699-2
Maryam Jafari, Alexa Delbosc, Graham Currie
{"title":"Factors associated with public transport use to school and non-school destinations among adolescents","authors":"Maryam Jafari, Alexa Delbosc, Graham Currie","doi":"10.1007/s11116-025-10699-2","DOIUrl":"https://doi.org/10.1007/s11116-025-10699-2","url":null,"abstract":"","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509380","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}
Pub Date : 2025-11-07DOI: 10.1007/s11116-025-10684-9
Manuel Ojeda-Cabral, Alexander D. Stead
When a new rail station is built on an existing line, it causes an increase in in-vehicle time – typically, between 2 and 4 min – for all existing trips that now stop at the new station. To the best of our knowledge, this paper is the first to investigate the impacts of these small time penalties on through-passenger demand. Cost-benefit appraisals of new stations in the UK have proven highly sensitive to the forecasts of through-passenger demand reductions. We conduct an ex-post analysis of the effect of a new station on through-passenger demand using a Difference-in-Differences approach. We fail to find a statistically significant impact of the station opening on through-passenger demand. If there are such demand impacts, our findings suggest that these are smaller in magnitude than the standard forecasting approach implies. More research is recommended to corroborate and generalise the findings to other contexts, as these have important implications for the planning and appraisal of new stations.
{"title":"Estimating the impact of new rail station openings on through-passenger demand: a difference-in-differences approach","authors":"Manuel Ojeda-Cabral, Alexander D. Stead","doi":"10.1007/s11116-025-10684-9","DOIUrl":"https://doi.org/10.1007/s11116-025-10684-9","url":null,"abstract":"When a new rail station is built on an existing line, it causes an increase in in-vehicle time – typically, between 2 and 4 min – for all existing trips that now stop at the new station. To the best of our knowledge, this paper is the first to investigate the impacts of these small time penalties on through-passenger demand. Cost-benefit appraisals of new stations in the UK have proven highly sensitive to the forecasts of through-passenger demand reductions. We conduct an ex-post analysis of the effect of a new station on through-passenger demand using a Difference-in-Differences approach. We fail to find a statistically significant impact of the station opening on through-passenger demand. If there are such demand impacts, our findings suggest that these are smaller in magnitude than the standard forecasting approach implies. More research is recommended to corroborate and generalise the findings to other contexts, as these have important implications for the planning and appraisal of new stations.","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"97 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455439","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}
Pub Date : 2025-11-07DOI: 10.1007/s11116-025-10687-6
Stefan S. Ivanovic, Milos Balac
Understanding the relationship between socio-economic factors and travel behavior is crucial for developing sustainable and inclusive transportation policies. This study analyzes mode choice, daily trip frequency, and daily travel time in Switzerland using high-resolution GPS data from the TimeUse+ project, which provides detailed mobility records from over 1,000 individuals. Unlike traditional survey-based studies, GPS tracking offers more precise insights into daily mobility patterns by reducing recall bias and capturing short trips often omitted in self-reported data. Our analysis reveals clear differences between weekday and weekend travel: On weekdays, socio-demographic variables (such as gender and age) together with transport supply variables (such as car ownership and public transport subscriptions) significantly shape mobility while on weekends, these effects largely weaken, and lifestyle-driven choices dominate. Notably, adults aged 66 + are the most mobile group on weekdays in terms of trip frequency, while individuals aged 56–65 take fewer but longer trips. Women make fewer trips overall, likely due to higher load of household-related responsibilities. Households with children reduce travel time but not trip frequency highlighting the complex effects of caregiving roles. Car access remains the strongest determinant of mode choice, with lower-income car owners showing higher car dependency than higher-income ones. Public transport subscriptions—especially monthly passes—strongly increase weekday transit use. On weekends, older adults and women show greater openness to sustainable modes like walking and cycling, indicating untapped potential for non-car mobility.
{"title":"Who travels more and longer in Switzerland? Insights into mode choice, daily travel time, and trip frequency from GPS-based data","authors":"Stefan S. Ivanovic, Milos Balac","doi":"10.1007/s11116-025-10687-6","DOIUrl":"https://doi.org/10.1007/s11116-025-10687-6","url":null,"abstract":"Understanding the relationship between socio-economic factors and travel behavior is crucial for developing sustainable and inclusive transportation policies. This study analyzes mode choice, daily trip frequency, and daily travel time in Switzerland using high-resolution GPS data from the TimeUse+ project, which provides detailed mobility records from over 1,000 individuals. Unlike traditional survey-based studies, GPS tracking offers more precise insights into daily mobility patterns by reducing recall bias and capturing short trips often omitted in self-reported data. Our analysis reveals clear differences between weekday and weekend travel: On weekdays, socio-demographic variables (such as gender and age) together with transport supply variables (such as car ownership and public transport subscriptions) significantly shape mobility while on weekends, these effects largely weaken, and lifestyle-driven choices dominate. Notably, adults aged 66 + are the most mobile group on weekdays in terms of trip frequency, while individuals aged 56–65 take fewer but longer trips. Women make fewer trips overall, likely due to higher load of household-related responsibilities. Households with children reduce travel time but not trip frequency highlighting the complex effects of caregiving roles. Car access remains the strongest determinant of mode choice, with lower-income car owners showing higher car dependency than higher-income ones. Public transport subscriptions—especially monthly passes—strongly increase weekday transit use. On weekends, older adults and women show greater openness to sustainable modes like walking and cycling, indicating untapped potential for non-car mobility.","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"129 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455434","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}
Pub Date : 2025-11-03DOI: 10.1007/s11116-025-10686-7
Amit Kumar, Vishrut S. Landge, Sumeet Jaiswal
{"title":"Shifts in shopping trips and multicategory interactions post COVID-19","authors":"Amit Kumar, Vishrut S. Landge, Sumeet Jaiswal","doi":"10.1007/s11116-025-10686-7","DOIUrl":"https://doi.org/10.1007/s11116-025-10686-7","url":null,"abstract":"","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"1 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427386","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}
Pub Date : 2025-11-03DOI: 10.1007/s11116-025-10689-4
Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth
There is a growing emphasis in urban centres on promoting sustainable mobility modes, particularly public transit systems. This highlights the critical need for predictive modelling frameworks that capture the local spatiotemporal dynamics of public transit to inform policy and planning decisions. This study develops a horizon-agnostic modelling framework using automated passenger count (APC) data from a public bus transit system, integrating machine learning (ML) and deep learning (DL) algorithms to forecast stop-level passenger counts and operational factors. We assess APC data quality, implement a feature-space optimisation pipeline to enhance algorithm-data fit, and use SHAP values to analyse feature attributions for model interpretability. Our analyses reveal a weak but asymmetric relationship between boarding and alighting passenger counts. Tree-based ML algorithms outperform DL algorithms due to the high proportion of categorical features, with Extreme Gradient Boosting (XGBoost) achieving the best performance. Furthermore, incorporating non-mobility data (weather, terrain, demographics, land use) improved modelling of passenger dynamics. However, stop-level modelling lacks inductive biases on the spatial structure of transit networks. The proposed framework provides policymakers and planners with data-driven tools to understand the local spatiotemporal dynamics of public transit under external influences, supporting resource allocation for stop placement, line routing, and bus scheduling. By predicting outcomes based on input feature combinations rather than specific temporal horizons, the framework enables scenario analysis for planning applications and can be embedded in digital twins and mobility dashboards to support informed commuting decisions by urban residents.
{"title":"Data-driven predictive modelling of stop-level public transit patterns","authors":"Oluwaleke Yusuf, Adil Rasheed, Frank Lindseth","doi":"10.1007/s11116-025-10689-4","DOIUrl":"https://doi.org/10.1007/s11116-025-10689-4","url":null,"abstract":"There is a growing emphasis in urban centres on promoting sustainable mobility modes, particularly public transit systems. This highlights the critical need for predictive modelling frameworks that capture the local spatiotemporal dynamics of public transit to inform policy and planning decisions. This study develops a horizon-agnostic modelling framework using automated passenger count (APC) data from a public bus transit system, integrating machine learning (ML) and deep learning (DL) algorithms to forecast stop-level passenger counts and operational factors. We assess APC data quality, implement a feature-space optimisation pipeline to enhance algorithm-data fit, and use SHAP values to analyse feature attributions for model interpretability. Our analyses reveal a weak but asymmetric relationship between boarding and alighting passenger counts. Tree-based ML algorithms outperform DL algorithms due to the high proportion of categorical features, with Extreme Gradient Boosting (XGBoost) achieving the best performance. Furthermore, incorporating non-mobility data (weather, terrain, demographics, land use) improved modelling of passenger dynamics. However, stop-level modelling lacks inductive biases on the spatial structure of transit networks. The proposed framework provides policymakers and planners with data-driven tools to understand the local spatiotemporal dynamics of public transit under external influences, supporting resource allocation for stop placement, line routing, and bus scheduling. By predicting outcomes based on input feature combinations rather than specific temporal horizons, the framework enables scenario analysis for planning applications and can be embedded in digital twins and mobility dashboards to support informed commuting decisions by urban residents.","PeriodicalId":49419,"journal":{"name":"Transportation","volume":"26 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427389","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：