Pub Date : 2026-04-01Epub Date: 2026-01-06DOI: 10.1016/j.tbs.2025.101230
Umer Mansoor , Yu Gu , Anthony Chen
Car ownership and car type choice are critical components of transportation planning, yet the interplay between these decisions and the impact of emerging vehicle technologies, such as autonomous vehicles (AVs), remain underexplored. As AVs become more prevalent, travelers’ perceptions and behaviors regarding their safety and security may affect their adoption. This study investigates how travelers’ risk perceptions (safety, security, and range anxiety) shape their joint decisions to own a car and select a vehicle type within a multimodal transportation system. We propose a discrete choice modeling-based equilibrium analysis framework that integrates a dogit model to capture captivity effects in car ownership decisions and a nested logit model to account for similarities among car types. The framework is formulated as a mathematical programming problem, ensuring solution existence and uniqueness. Numerical experiments on a toy network and a real-world case study reveal that reductions in travelers’ risk perceptions toward AVs lead to significant increases in AV adoption, highlighting the critical role of public trust in transitioning to AV-dominated markets. By explicitly linking risk perceptions to long-term transportation planning, this model equips policymakers with a tool to design strategies that address behavioral barriers to AV adoption while balancing efficiency and safety objectives.
{"title":"Modeling travelers’ joint car ownership and car type choice behavior: The role of autonomous vehicle safety-security perceptions","authors":"Umer Mansoor , Yu Gu , Anthony Chen","doi":"10.1016/j.tbs.2025.101230","DOIUrl":"10.1016/j.tbs.2025.101230","url":null,"abstract":"<div><div>Car ownership and car type choice are critical components of transportation planning, yet the interplay between these decisions and the impact of emerging vehicle technologies, such as autonomous vehicles (AVs), remain underexplored. As AVs become more prevalent, travelers’ perceptions and behaviors regarding their safety and security may affect their adoption. This study investigates how travelers’ risk perceptions (safety, security, and range anxiety) shape their joint decisions to own a car and select a vehicle type within a multimodal transportation system. We propose a discrete choice modeling-based equilibrium analysis framework that integrates a dogit model to capture captivity effects in car ownership decisions and a nested logit model to account for similarities among car types. The framework is formulated as a mathematical programming problem, ensuring solution existence and uniqueness. Numerical experiments on a toy network and a real-world case study reveal that reductions in travelers’ risk perceptions toward AVs lead to significant increases in AV adoption, highlighting the critical role of public trust in transitioning to AV-dominated markets. By explicitly linking risk perceptions to long-term transportation planning, this model equips policymakers with a tool to design strategies that address behavioral barriers to AV adoption while balancing efficiency and safety objectives.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101230"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924327","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 : 2026-04-01Epub Date: 2025-11-10DOI: 10.1016/j.tbs.2025.101165
Zhe Wang, Zhixiang Fang
Bus operation control is an effective way to enhance bus line operational efficiency and provide high-quality intra-city transit services. Traditional bus operation control approaches predominantly focus on static control, while the dynamic nature of transit ridership necessitates real-time control approaches. This study proposes a novel multi-agent Deep Reinforcement Learning (DRL) approach to address the real-time bus operation control problem, using a hybrid strategy that combines departure timetabling, stop-skipping, and re-routing. In this approach, the single-line bus operational process is modelled as a Markov Decision Process (MDP), where the reward function considers both bus operational costs and passenger waiting time. Using a real-world transportation dataset in Xiamen, China, the experiments verified that our approach is able to reduce the passenger waiting time without higher operational costs and exhibit robustness on bus lines with heavy ridership demand and uneven ridership distribution. This study presents a pioneering endeavour in integrating DRL and transportation geographic information system into bus operation control. The real-time control mechanism enables bus lines to dynamically adapt to ridership demand fluctuations and maintain passenger satisfaction across diverse scenarios.
{"title":"A deep reinforcement learning approach for real-time bus operation control using departure timetabling, stop-skipping, and re-routing","authors":"Zhe Wang, Zhixiang Fang","doi":"10.1016/j.tbs.2025.101165","DOIUrl":"10.1016/j.tbs.2025.101165","url":null,"abstract":"<div><div>Bus operation control is an effective way to enhance bus line operational efficiency and provide high-quality intra-city transit services. Traditional bus operation control approaches predominantly focus on static control, while the dynamic nature of transit ridership necessitates real-time control approaches. This study proposes a novel multi-agent Deep Reinforcement Learning (DRL) approach to address the real-time bus operation control problem, using a hybrid strategy that combines departure timetabling, stop-skipping, and re-routing. In this approach, the single-line bus operational process is modelled as a Markov Decision Process (MDP), where the reward function considers both bus operational costs and passenger waiting time. Using a real-world transportation dataset in Xiamen, China, the experiments verified that our approach is able to reduce the passenger waiting time without higher operational costs and exhibit robustness on bus lines with heavy ridership demand and uneven ridership distribution. This study presents a pioneering endeavour in integrating DRL and transportation geographic information system into bus operation control. The real-time control mechanism enables bus lines to dynamically adapt to ridership demand fluctuations and maintain passenger satisfaction across diverse scenarios.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101165"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145485602","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 : 2026-04-01Epub Date: 2025-12-18DOI: 10.1016/j.tbs.2025.101215
Zahid Hussain , Wael K.M. Alhajyaseen , Mohammad N.H. Naser , Qinaat Hussain , Charitha Dias , Miho Iryo-Asano
Despite growing global interest in e-scooters as micromobility solutions, limited research has explored factors influencing their adoption in car-dependent, high-income contexts with extreme summer climates. This study addresses this gap through nationwide web-based survey in Qatar, where high private vehicle dependency, summer temperatures exceeding 45 °C, and limited cycling infrastructure as well as limited cycling culture create unique challenges for micromobility integration. The final sample consisted of 2736 respondents (339 e-scooter users and 2397 non-users), capturing usage patterns, demographic information, and non-users’ perceptions of public acceptance and intention to use e-scooters. Among current users, e-scooters were predominantly used for leisure and commuting, with males and notably, individuals without driving licenses using them frequently. Usage patterns differed between ownership types, with shared/rental users predominantly using e-scooters for leisure, while owned e-scooter users primarily used them for commuting. To examine non-users’ perspectives, structural equation modeling was used to assess influence of different factors on usage intention and perceived public acceptance. Findings revealed that regulatory and infrastructure support, along with social influence and preference, were the most significant predictors, while cost and service quality barriers negatively influenced usage intention. Importantly, perceived public acceptance strongly influenced personal intention to use, demonstrating that social legitimacy substantially shapes adoption even in car-oriented contexts. Sociodemographic analysis revealed that car ownership and higher income negatively predicted adoption, while non-license holders, non-Arab residents, and employed individuals showed significantly higher adoption potential. These findings offer valuable insights for policymakers and urban planners in developing targeted interventions to promote safe and sustainable integration of e-scooters. Such interventions include improved infrastructure, effective regulations, competitive pricing, enhanced service quality, and community engagement initiatives. While grounded in Qatar’s context, these findings can be generalized to urban environments globally characterized by high motorization rates, cultural preferences for private vehicles, challenging climatic conditions, and infrastructure limitations.
{"title":"E-scooters in Qatar: Public perception, adoption intentions, and implications for urban mobility policy","authors":"Zahid Hussain , Wael K.M. Alhajyaseen , Mohammad N.H. Naser , Qinaat Hussain , Charitha Dias , Miho Iryo-Asano","doi":"10.1016/j.tbs.2025.101215","DOIUrl":"10.1016/j.tbs.2025.101215","url":null,"abstract":"<div><div>Despite growing global interest in e-scooters as micromobility solutions, limited research has explored factors influencing their adoption in car-dependent, high-income contexts with extreme summer climates. This study addresses this gap through nationwide web-based survey in Qatar, where high private vehicle dependency, summer temperatures exceeding 45 °C, and limited cycling infrastructure as well as limited cycling culture create unique challenges for micromobility integration. The final sample consisted of 2736 respondents (339 e-scooter users and 2397 non-users), capturing usage patterns, demographic information, and non-users’ perceptions of public acceptance and intention to use e-scooters. Among current users, e-scooters were predominantly used for leisure and commuting, with males and notably, individuals without driving licenses using them frequently. Usage patterns differed between ownership types, with shared/rental users predominantly using e-scooters for leisure, while owned e-scooter users primarily used them for commuting. To examine non-users’ perspectives, structural equation modeling was used to assess influence of different factors on usage intention and perceived public acceptance. Findings revealed that regulatory and infrastructure support, along with social influence and preference, were the most significant predictors, while cost and service quality barriers negatively influenced usage intention. Importantly, perceived public acceptance strongly influenced personal intention to use, demonstrating that social legitimacy substantially shapes adoption even in car-oriented contexts. Sociodemographic analysis revealed that car ownership and higher income negatively predicted adoption, while non-license holders, non-Arab residents, and employed individuals showed significantly higher adoption potential. These findings offer valuable insights for policymakers and urban planners in developing targeted interventions to promote safe and sustainable integration of e-scooters. Such interventions include improved infrastructure, effective regulations, competitive pricing, enhanced service quality, and community engagement initiatives. While grounded in Qatar’s context, these findings can be generalized to urban environments globally characterized by high motorization rates, cultural preferences for private vehicles, challenging climatic conditions, and infrastructure limitations.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101215"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786366","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 : 2026-04-01Epub Date: 2026-01-07DOI: 10.1016/j.tbs.2026.101232
Jiayu Pan , Leonel Aguilar , Michal Gath-Morad , Ronita Bardhan , Koen Steemers
Cruise ship environments have been identified as high-risk settings for communicable disease outbreaks. The severe COVID-19 outbreaks aboard several cruise ships have further intensified concerns about transmission in these confined, densely populated environments. Although some behavioural interventions have been studied, the influence of spatial and operational design on transmission risk remains under explored.
To address this gap, this study applies agent-based modelling (ABM) to simulate passenger movement patterns on mixed-use cruise ship decks as a surrogate for infection transmission. By modelling individual movement behaviours and collisions (close-contact exposure) within the mixed-use decks, the ABM approach supports the development of spatial design and operational strategies that can reduce transmission risk and improve the efficiency and healthiness of onboard circulation. We conducted verification and parametric tests to assess model performance and designed three experiments to evaluate the effects of varying occupancy levels, infection prevalence, spatial layouts and access restriction strategies with 1181 simulation runs, the simulation results are complemented by spatial analyses of deck plans to inform evidence-based recommendations for safer cruise ship environments.
We identified three key spatial drivers of disease transmission risk on cruise ships. First, higher occupancy density and compact layouts significantly increased close-contact events, as passengers navigated narrow, poorly connected corridors. Second, the effectiveness of quarantine interventions depended not just on their presence but on their spatial placement: centrally located restrictions amplified congestion, while peripheral placement helped alleviate it. Third, simple passive design changes—such as widening corridors or enhancing internal connectivity—reduced movement bottlenecks and collisions, without requiring behavioural adaptation. Together, these findings demonstrate that spatial configuration is not merely a backdrop but a powerful determinant of health resilience in high-occupancy environments.
{"title":"Spatial and operational interventions for healthy cruise ship design using agent-based modelling","authors":"Jiayu Pan , Leonel Aguilar , Michal Gath-Morad , Ronita Bardhan , Koen Steemers","doi":"10.1016/j.tbs.2026.101232","DOIUrl":"10.1016/j.tbs.2026.101232","url":null,"abstract":"<div><div>Cruise ship environments have been identified as high-risk settings for communicable disease outbreaks. The severe COVID-19 outbreaks aboard several cruise ships have further intensified concerns about transmission in these confined, densely populated environments. Although some behavioural interventions have been studied, the influence of spatial and operational design on transmission risk remains under explored.</div><div>To address this gap, this study applies agent-based modelling (ABM) to simulate passenger movement patterns on mixed-use cruise ship decks as a surrogate for infection transmission. By modelling individual movement behaviours and collisions (close-contact exposure) within the mixed-use decks, the ABM approach supports the development of spatial design and operational strategies that can reduce transmission risk and improve the efficiency and healthiness of onboard circulation. We conducted verification and parametric tests to assess model performance and designed three experiments to evaluate the effects of varying occupancy levels, infection prevalence, spatial layouts and access restriction strategies with 1181 simulation runs, the simulation results are complemented by spatial analyses of deck plans to inform evidence-based recommendations for safer cruise ship environments.</div><div>We identified three key spatial drivers of disease transmission risk on cruise ships. First, higher occupancy density and compact layouts significantly increased close-contact events, as passengers navigated narrow, poorly connected corridors. Second, the effectiveness of quarantine interventions depended not just on their presence but on their spatial placement: centrally located restrictions amplified congestion, while peripheral placement helped alleviate it. Third, simple passive design changes—such as widening corridors or enhancing internal connectivity—reduced movement bottlenecks and collisions, without requiring behavioural adaptation. Together, these findings demonstrate that spatial configuration is not merely a backdrop but a powerful determinant of health resilience in high-occupancy environments.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101232"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924330","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 : 2026-04-01Epub Date: 2025-12-01DOI: 10.1016/j.tbs.2025.101196
Karl Samuelsson , Ioar Rivas , Marta Cirach , Bruno Raimbault , Alan Domínguez , Yu Zhao , Toni Galmés , Antònia Valentin , Maria Foraster , Mireia Gascon , Cecilia Persavento , Maria Dolores Gomez Roig , Elisa Llurba , Efstathios Boukouras , Oriol Marquet , Monika Maciejewska , Mark J. Nieuwenhuijsen , Xavier Basagaña , Jordi Sunyer , Achilleas Psyllidis , Payam Dadvand
Understanding the factors that shape daily mobility during pregnancy is essential for inclusive transportation planning that promotes active travel for all. Using smartphone-based Global Positioning System data from 860 pregnant women in Barcelona, Spain, we evaluated the correlates of active and passive travel in early and late pregnancy. We identified 33 correlates from 48 candidate variables including personal characteristics, the residential physical environment, the social environment, and temporal factors. The most important correlate across pregnancy was non-European ethnic origin, being associated with 10–15 min less daily active travel. In early pregnancy, commuting distance was the most important correlate, being positively associated with passive travel, while the COVID-19 pandemic was associated with less passive travel. In late pregnancy, residential walkability and having a university degree were positively associated with active travel. The neighbourhood education level was associated with more active travel, particularly during weekends. We discuss key priorities for supporting active travel during pregnancy.
{"title":"Correlates of pregnant women’s active and passive mobility: A smartphone-based tracking study in Barcelona, Spain","authors":"Karl Samuelsson , Ioar Rivas , Marta Cirach , Bruno Raimbault , Alan Domínguez , Yu Zhao , Toni Galmés , Antònia Valentin , Maria Foraster , Mireia Gascon , Cecilia Persavento , Maria Dolores Gomez Roig , Elisa Llurba , Efstathios Boukouras , Oriol Marquet , Monika Maciejewska , Mark J. Nieuwenhuijsen , Xavier Basagaña , Jordi Sunyer , Achilleas Psyllidis , Payam Dadvand","doi":"10.1016/j.tbs.2025.101196","DOIUrl":"10.1016/j.tbs.2025.101196","url":null,"abstract":"<div><div>Understanding the factors that shape daily mobility during pregnancy is essential for inclusive transportation planning that promotes active travel for all. Using smartphone-based Global Positioning System data from 860 pregnant women in Barcelona, Spain, we evaluated the correlates of active and passive travel in early and late pregnancy. We identified 33 correlates from 48 candidate variables including personal characteristics, the residential physical environment, the social environment, and temporal factors. The most important correlate across pregnancy was non-European ethnic origin, being associated with 10–15 min less daily active travel. In early pregnancy, commuting distance was the most important correlate, being positively associated with passive travel, while the COVID-19 pandemic was associated with less passive travel. In late pregnancy, residential walkability and having a university degree were positively associated with active travel. The neighbourhood education level was associated with more active travel, particularly during weekends. We discuss key priorities for supporting active travel during pregnancy.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101196"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651526","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}
Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward function to represent preferences and a policy function to replicate observed behavior using deep neural networks (DNNs). However, most DIRL applications emphasize prediction accuracy and treat the learned functions as black boxes, offering limited behavioral insight. To address this gap, we propose an interpretable DIRL framework that adapts an adversarial IRL approach for modeling sequential activity-travel behavior. Interpretability is achieved in two ways: (1) we distill the learned policy into a surrogate interpretable Multinomial Logit (MNL) model, enabling the extraction of behavioral drivers from model parameters; and (2) we derive short-term rewards and long-term returns from the learned reward function, quantifying immediate preferences and overall decision outcomes across activity sequences. Applied to real-world travel survey data from Singapore, our framework uncovers meaningful behavioral patterns. The MNL-based surrogate model reveals that travel decisions are shaped by activity schedules, travel time, and socio-demographic attributes, particularly employment type. Reward and return analysis distinguish returners with regular patterns from explorers with irregular ones. Regular patterns yield higher long-term returns, while females and elderly individuals exhibit lower returns, indicating disparities in individual activity patterns. These findings bridge the gap between theory-driven behavioral models and data-driven machine learning, offering actionable insights for transport policy and urban planning.
{"title":"Analyzing sequential activity and travel decisions with interpretable deep inverse reinforcement learning","authors":"Yuebing Liang , Shenhao Wang , Jiangbo Yu , Zhan Zhao , Jinhua Zhao , Sandy Pentland","doi":"10.1016/j.tbs.2025.101171","DOIUrl":"10.1016/j.tbs.2025.101171","url":null,"abstract":"<div><div>Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward function to represent preferences and a policy function to replicate observed behavior using deep neural networks (DNNs). However, most DIRL applications emphasize prediction accuracy and treat the learned functions as black boxes, offering limited behavioral insight. To address this gap, we propose an interpretable DIRL framework that adapts an adversarial IRL approach for modeling sequential activity-travel behavior. Interpretability is achieved in two ways: (1) we distill the learned policy into a surrogate interpretable Multinomial Logit (MNL) model, enabling the extraction of behavioral drivers from model parameters; and (2) we derive short-term rewards and long-term returns from the learned reward function, quantifying immediate preferences and overall decision outcomes across activity sequences. Applied to real-world travel survey data from Singapore, our framework uncovers meaningful behavioral patterns. The MNL-based surrogate model reveals that travel decisions are shaped by activity schedules, travel time, and socio-demographic attributes, particularly employment type. Reward and return analysis distinguish returners with regular patterns from explorers with irregular ones. Regular patterns yield higher long-term returns, while females and elderly individuals exhibit lower returns, indicating disparities in individual activity patterns. These findings bridge the gap between theory-driven behavioral models and data-driven machine learning, offering actionable insights for transport policy and urban planning.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101171"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521179","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 : 2026-04-01Epub Date: 2025-11-13DOI: 10.1016/j.tbs.2025.101191
Milad Malekzadeh , Darja Reuschke , Jed A. Long
How wellbeing can be improved in cities, has attracted increasing attention. This paper studies urban stress and happiness in relation to daily travel behaviour through a large app-based geographic ecological momentary assessment study conducted in three English cities: Birmingham, Leeds, and Brighton and Hove. The key questions are whether, and to what extent, environmental factors—specifically, green and blue spaces, and weather conditions—affect urban travellers’ happiness and stress levels immediately following travel. GPS data from 606 participants were collected and combined with survey questions asking participants to score their current happiness and stress levels at the end of trips. Environmental data were linked to the GPS location data. The results indicate that exposure to green and blue spaces during trips had no immediate effect on happiness or stress levels. However, active transportation modes, such as walking and biking, were associated with higher happiness and lower stress compared to car use. These findings suggest that while exposure to green and blue spaces may provide long-term environmental values within an urban context; promoting active travel modes could yield more immediate benefits for urban wellbeing.
{"title":"Exposure to green and blue spaces during travel does not have immediate effect on subjective happiness and stress: evidence from a GPS survey in England","authors":"Milad Malekzadeh , Darja Reuschke , Jed A. Long","doi":"10.1016/j.tbs.2025.101191","DOIUrl":"10.1016/j.tbs.2025.101191","url":null,"abstract":"<div><div>How wellbeing can be improved in cities, has attracted increasing attention. This paper studies urban stress and happiness in relation to daily travel behaviour through a large app-based geographic ecological momentary assessment study conducted in three English cities: Birmingham, Leeds, and Brighton and Hove. The key questions are whether, and to what extent, environmental factors—specifically, green and blue spaces, and weather conditions—affect urban travellers’ happiness and stress levels immediately following travel. GPS data from 606 participants were collected and combined with survey questions asking participants to score their current happiness and stress levels at the end of trips. Environmental data were linked to the GPS location data. The results indicate that exposure to green and blue spaces during trips had no immediate effect on happiness or stress levels. However, active transportation modes, such as walking and biking, were associated with higher happiness and lower stress compared to car use. These findings suggest that while exposure to green and blue spaces may provide long-term environmental values within an urban context; promoting active travel modes could yield more immediate benefits for urban wellbeing.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101191"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521180","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 : 2026-04-01Epub Date: 2025-11-21DOI: 10.1016/j.tbs.2025.101176
Jieyuan Lan , Tao Feng
The paper aims to investigate the interdependent choice behavior of individuals regarding Remote Working Centers (RWCs) and transportation modes, focusing particularly on latent factors like attitudes and personality traits. Using stated preference data from Tokyo, Japan, an integrated choice and latent variable (ICLV) model was applied. The results reveal that RWCs with six square meters of workspaces, minimal distractions, and no nearby amenities increase the probability of using RWCs. Work commitment, opportunity loss, workplace attire, timesaving attitude, workplace attachment, and workplace aversion significantly influence RWC usage. Individuals having higher work commitment, greater concerns about opportunity loss, higher workplace attire, and stronger workplace attachment and aversion are more likely to use RWCs, while timesaving people prefer working from home. Transportation choices also vary, with workplace attire investments favoring driving, and timesaving attitudes leaning toward walking or rail. These findings provide valuable insights for policymakers and stakeholders to promote teleworking, alleviating traffic and reducing environmental impacts.
{"title":"Modeling interdependent choices of remote working centers and transportation with attitudes through latent variables","authors":"Jieyuan Lan , Tao Feng","doi":"10.1016/j.tbs.2025.101176","DOIUrl":"10.1016/j.tbs.2025.101176","url":null,"abstract":"<div><div>The paper aims to investigate the interdependent choice behavior of individuals regarding Remote Working Centers (RWCs) and transportation modes, focusing particularly on latent factors like attitudes and personality traits. Using stated preference data from Tokyo, Japan, an integrated choice and latent variable (ICLV) model was applied. The results reveal that RWCs with six square meters of workspaces, minimal distractions, and no nearby amenities increase the probability of using RWCs. Work commitment, opportunity loss, workplace attire, timesaving attitude, workplace attachment, and workplace aversion significantly influence RWC usage. Individuals having higher work commitment, greater concerns about opportunity loss, higher workplace attire, and stronger workplace attachment and aversion are more likely to use RWCs, while timesaving people prefer working from home. Transportation choices also vary, with workplace attire investments favoring driving, and timesaving attitudes leaning toward walking or rail. These findings provide valuable insights for policymakers and stakeholders to promote teleworking, alleviating traffic and reducing environmental impacts.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101176"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571790","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 : 2026-04-01Epub Date: 2025-12-23DOI: 10.1016/j.tbs.2025.101214
Wenxiang Li , Bo Liu , Weiwei Liu , Yang Yang
The rapid growth of ride-hailing services has reshaped urban mobility but also intensified congestion, emissions, and car dependency, raising concerns about sustainability. The metro-integrated multimodal travel (MIMT), enabled by Mobility as a Service (MaaS) platforms, offers promising low-carbon alternatives by integrating metro systems with convenient access and egress modes such as ridesplitting, buses, cycling, and walking. This study aims to develop a pratical analytical framework to assess the substitution potential of MIMT for ride-hailing trips in the era of MaaS. First, a trip reconstruction method is proposed to generate ten types of MIMT alternatives under identical origin–destination (OD) conditions. Second, a multidimensional evaluation model is established to quantify substitution benefits by jointly considering cost savings, carbon emission reductions, and time delays. Third, an interpretable machine learning approach (CatBoost integrated with SHAP and PDP) is applied to identify the travel and built environment factors influencing substitution potential. A case study based on 837,503 ride-hailing trips in Shanghai indicates that 56.46 % of trips could feasibly be replaced by MIMT alternatives. On average, each substituted trip yields a comprehensive benefits of 13.83 CNY, comprising cost savings of 24.03 CNY and carbon emission reductions of 1.29 kg, at the cost of an average travel time increase of 17.51 min. The results further reveal that substitution potential is primarily driven by route nonlinearity, trip distance, and metro accessibility. Travel-related variables account for 61.11 % of explanatory power, while built environment features contribute the remaining 38.89 %. Sensitivity analyses demonstrate that travelers' transition from ride-hailing to MIMT is predominantly influenced by the value of time, with current carbon pricing exerting only a marginal effect. These findings highlight the role of MaaS in promoting multimodal integration and provide actionable insights for policymakers and platform operators to reduce ride-hailing dependency and advance low-carbon urban mobility.
{"title":"How can metro-integrated multimodal travel substitute for ride-hailing trips in the era of mobility as a service?","authors":"Wenxiang Li , Bo Liu , Weiwei Liu , Yang Yang","doi":"10.1016/j.tbs.2025.101214","DOIUrl":"10.1016/j.tbs.2025.101214","url":null,"abstract":"<div><div>The rapid growth of ride-hailing services has reshaped urban mobility but also intensified congestion, emissions, and car dependency, raising concerns about sustainability. The metro-integrated multimodal travel (MIMT), enabled by Mobility as a Service (MaaS) platforms, offers promising low-carbon alternatives by integrating metro systems with convenient access and egress modes such as ridesplitting, buses, cycling, and walking. This study aims to develop a pratical analytical framework to assess the substitution potential of MIMT for ride-hailing trips in the era of MaaS. First, a trip reconstruction method is proposed to generate ten types of MIMT alternatives under identical origin–destination (OD) conditions. Second, a multidimensional evaluation model is established to quantify substitution benefits by jointly considering cost savings, carbon emission reductions, and time delays. Third, an interpretable machine learning approach (CatBoost integrated with SHAP and PDP) is applied to identify the travel and built environment factors influencing substitution potential. A case study based on 837,503 ride-hailing trips in Shanghai indicates that 56.46 % of trips could feasibly be replaced by MIMT alternatives. On average, each substituted trip yields a comprehensive benefits of 13.83 CNY, comprising cost savings of 24.03 CNY and carbon emission reductions of 1.29 kg, at the cost of an average travel time increase of 17.51 min. The results further reveal that substitution potential is primarily driven by route nonlinearity, trip distance, and metro accessibility. Travel-related variables account for 61.11 % of explanatory power, while built environment features contribute the remaining 38.89 %. Sensitivity analyses demonstrate that travelers' transition from ride-hailing to MIMT is predominantly influenced by the value of time, with current carbon pricing exerting only a marginal effect. These findings highlight the role of MaaS in promoting multimodal integration and provide actionable insights for policymakers and platform operators to reduce ride-hailing dependency and advance low-carbon urban mobility.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101214"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822775","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 : 2026-04-01Epub Date: 2025-12-30DOI: 10.1016/j.tbs.2025.101220
Mira Simunic , Mingyue Selena Sheng , Le Wen , Ramesh Chandra Majhi , Prakash Ranjitkar , Bo Du , Minh Kieu , Basil Sharp , Douglas Wilson
This research delves into consumer behavior in the economic domain, particularly focusing on adopting innovative technologies. It assesses the perceived economic and environmental advantages of dynamic wireless power transfer technology, known as dynamic wireless charging. This technology allows electric vehicles to be charged while in motion, which could significantly influence their adoption rates in Aotearoa, New Zealand. The study employs discrete choice modeling to gain insights into consumer valuation of dynamic charging technologies. Various sophisticated logit models were utilized to analyze the data gathered from surveys on consumer preferences rigorously. These models, such as multinomial logit, heteroscedastic logit, and mixed logit, allow for a nuanced understanding of consumer choices by accommodating varying levels of randomness and heterogeneity in decision-making processes. Furthermore, the research investigates the willingness to pay among users, which indirectly measures how much consumers value the ability to charge their vehicles dynamically. The key finding from the study is that the convenience of being able to charge while driving is a significant factor that enhances the adoption of electric vehicles. This indicates that as dynamic wireless charging technology becomes more widespread and accessible, it could be crucial in accelerating the transition towards electric mobility, particularly in contexts where environmental sustainability and technological innovation are prioritized.
{"title":"Driving adoption: Discrete choice modeling of consumer valuation for wireless electric vehicles charging in Aotearoa, New Zealand","authors":"Mira Simunic , Mingyue Selena Sheng , Le Wen , Ramesh Chandra Majhi , Prakash Ranjitkar , Bo Du , Minh Kieu , Basil Sharp , Douglas Wilson","doi":"10.1016/j.tbs.2025.101220","DOIUrl":"10.1016/j.tbs.2025.101220","url":null,"abstract":"<div><div>This research delves into consumer behavior in the economic domain, particularly focusing on adopting innovative technologies. It assesses the perceived economic and environmental advantages of dynamic wireless power transfer technology, known as dynamic wireless charging. This technology allows electric vehicles to be charged while in motion, which could significantly influence their adoption rates in Aotearoa, New Zealand. The study employs discrete choice modeling to gain insights into consumer valuation of dynamic charging technologies. Various sophisticated logit models were utilized to analyze the data gathered from surveys on consumer preferences rigorously. These models, such as multinomial logit, heteroscedastic logit, and mixed logit, allow for a nuanced understanding of consumer choices by accommodating varying levels of randomness and heterogeneity in decision-making processes. Furthermore, the research investigates the willingness to pay among users, which indirectly measures how much consumers value the ability to charge their vehicles dynamically. The key finding from the study is that the convenience of being able to charge while driving is a significant factor that enhances the adoption of electric vehicles. This indicates that as dynamic wireless charging technology becomes more widespread and accessible, it could be crucial in accelerating the transition towards electric mobility, particularly in contexts where environmental sustainability and technological innovation are prioritized.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101220"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883686","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}
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