Pub Date : 2026-04-01Epub Date: 2026-01-14DOI: 10.1016/j.tbs.2026.101236
Junmin Lee , Harin Chae , Woojin Bang , Daeho Lee , Myoungjin Oh , Jungwoo Shin , Keungoui Kim
The emergence of Urban Air Mobility (UAM) as a transformative mode of transportation is driven by the convergence of advanced information technologies, including automation and autonomous flight systems, with innovative aerial vehicle designs. Positioned as a key component of future smart and interconnected urban mobility ecosystems, UAM has attracted growing attention from both industry and policymakers. This study examines consumer preferences for UAM in South Korea using a choice-based conjoint analysis. The analysis quantifies the relative importance of key service attributes—transit time, price, operating speed, autonomy, waiting time, seat width, and luggage weight—in influencing potential adoption. The results show that transit time is the most influential attribute shaping consumer preferences, followed by price, operating speed, and autonomy. Secondary factors such as waiting time, seat width, and luggage weight also contribute to perceived service quality and convenience. These findings provide actionable insights for UAM service providers and policymakers, highlighting the need to balance efficiency, affordability, and technological advancement to foster consumer acceptance and sustainable market integration. Future research should validate these findings across different cultural and urban contexts to assess the generalisability of consumer preference structures.
{"title":"Understanding consumer preferences for Urban Air Mobility: A choice-based conjoint analysis approach with the case of South Korea","authors":"Junmin Lee , Harin Chae , Woojin Bang , Daeho Lee , Myoungjin Oh , Jungwoo Shin , Keungoui Kim","doi":"10.1016/j.tbs.2026.101236","DOIUrl":"10.1016/j.tbs.2026.101236","url":null,"abstract":"<div><div>The emergence of Urban Air Mobility (UAM) as a transformative mode of transportation is driven by the convergence of advanced information technologies, including automation and autonomous flight systems, with innovative aerial vehicle designs. Positioned as a key component of future smart and interconnected urban mobility ecosystems, UAM has attracted growing attention from both industry and policymakers. This study examines consumer preferences for UAM in South Korea using a choice-based conjoint analysis. The analysis quantifies the relative importance of key service attributes—transit time, price, operating speed, autonomy, waiting time, seat width, and luggage weight—in influencing potential adoption. The results show that transit time is the most influential attribute shaping consumer preferences, followed by price, operating speed, and autonomy. Secondary factors such as waiting time, seat width, and luggage weight also contribute to perceived service quality and convenience. These findings provide actionable insights for UAM service providers and policymakers, highlighting the need to balance efficiency, affordability, and technological advancement to foster consumer acceptance and sustainable market integration. Future research should validate these findings across different cultural and urban contexts to assess the generalisability of consumer preference structures.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101236"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976682","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: 2025-11-18DOI: 10.1016/j.tbs.2025.101192
Sung-Yong Choi , Jae-Jang Yang , Muhammad Shakil Ahmad , Yong-Ki Lee
The purpose of this study is to examine value elements related to hybrid electric vehicles and their impact on consumers’ brand-related (brand identification) and corporate-related (corporate image) responses, which are expected to influence buying intention. Data was collected from 294 owners of hybrid electric vehicles in South Korea and analyzed using PLS-SEM and NCA (necessary condition analysis). The study finds that all four elements of value have a significant impact on either brand identification or corporate image. The study shows that brand identification and corporate image predict buying intention. Brand identification is found to play a mediating role in the relationship between aesthetic value and corporate image and between eco-friendliness and corporate image. The study finds that corporate image mediates the relationship between brand identification and buying intention. The study contributes to the understanding of the psychological process that explains buying intention of the hybrid electric vehicle (HEV) users.
{"title":"Identifying necessary and sufficient conditions for enhancing loyalty in hybrid electronic vehicles: A combined PLS-SEM and NCA approach","authors":"Sung-Yong Choi , Jae-Jang Yang , Muhammad Shakil Ahmad , Yong-Ki Lee","doi":"10.1016/j.tbs.2025.101192","DOIUrl":"10.1016/j.tbs.2025.101192","url":null,"abstract":"<div><div>The purpose of this study is to examine value elements related to hybrid electric vehicles and their impact on consumers’ brand-related (brand identification) and corporate-related (corporate image) responses, which are expected to influence buying intention. Data was collected from 294 owners of hybrid electric vehicles in South Korea and analyzed using PLS-SEM and NCA (necessary condition analysis). The study finds that all four elements of value have a significant impact on either brand identification or corporate image. The study shows that brand identification and corporate image predict buying intention. Brand identification is found to play a mediating role in the relationship between aesthetic value and corporate image and between eco-friendliness and corporate image. The study finds that corporate image mediates the relationship between brand identification and buying intention. The study contributes to the understanding of the psychological process that explains buying intention of the hybrid electric vehicle (HEV) users.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101192"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553905","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: 2025-12-08DOI: 10.1016/j.tbs.2025.101209
Yongtao Liu , Jing Feng , Dongdong Song , Yitao Yang , Danyue Zhi , Huan Pang , Deyin Jiang , Shifeng Niu
Hazard-based duration models have gained popularity in predicting traffic incident durations. However, most studies analyze congestion duration as a whole, overlooking the varying levels of congestion (minor, moderate, and severe), which may be interrelated and influenced by different factors. This study proposes multivariate joint survival analysis models to examine the relationships across these congestion levels using traffic incident data from New York State (2017–2019), treating minor and moderate congestion as recurrent events, with severe congestion as a terminal event. By incorporating a frailty term, unobserved heterogeneity among road segments is accounted for. The results show that real-time weather factors, such as temperature, wind speed, visibility, and precipitation (rain/snowfall), exhibit varying effects on the duration of different congestion levels, with these effects fluctuating over time. For example, in 2017–2019, low temperatures increase the duration of minor congestion by 40.88 %, 26.66 %, and 52.69 %, respectively. Conversely, for severe congestion, low temperatures also show stable temporal effects but reduce congestion duration by 70.81 %, 60.07 %, and 70.81 %, respectively. Rainy weather increases the duration of moderate congestion by 54.10 %, 31.94 %, and 54.10 %, respectively, while snowy weather reduces it by 41.38 %, 37.19 %, and 27.48 %. More importantly, a significant correlation is found between minor or moderate congestion, which are recurrent events, and severe congestion, the terminal event. Furthermore, a positive correlation between minor and moderate congestion suggests that unobserved factors jointly influence the duration of both. The study confirms the superiority of the proposed joint model for analyzing traffic incident duration and provides practical insights for transportation policymakers to massively ease congestion more effectively.
{"title":"Exploring weather-related factors affecting the duration of multiple congestion levels caused by traffic incidents using a multivariate joint frailty survival model","authors":"Yongtao Liu , Jing Feng , Dongdong Song , Yitao Yang , Danyue Zhi , Huan Pang , Deyin Jiang , Shifeng Niu","doi":"10.1016/j.tbs.2025.101209","DOIUrl":"10.1016/j.tbs.2025.101209","url":null,"abstract":"<div><div>Hazard-based duration models have gained popularity in predicting traffic incident durations. However, most studies analyze congestion duration as a whole, overlooking the varying levels of congestion (minor, moderate, and severe), which may be interrelated and influenced by different factors. This study proposes multivariate joint survival analysis models to examine the relationships across these congestion levels using traffic incident data from New York State (2017–2019), treating minor and moderate congestion as recurrent events, with severe congestion as a terminal event. By incorporating a frailty term, unobserved heterogeneity among road segments is accounted for. The results show that real-time weather factors, such as temperature, wind speed, visibility, and precipitation (rain/snowfall), exhibit varying effects on the duration of different congestion levels, with these effects fluctuating over time. For example, in 2017–2019, low temperatures increase the duration of minor congestion by 40.88 %, 26.66 %, and 52.69 %, respectively. Conversely, for severe congestion, low temperatures also show stable temporal effects but reduce congestion duration by 70.81 %, 60.07 %, and 70.81 %, respectively. Rainy weather increases the duration of moderate congestion by 54.10 %, 31.94 %, and 54.10 %, respectively, while snowy weather reduces it by 41.38 %, 37.19 %, and 27.48 %. More importantly, a significant correlation is found between minor or moderate congestion, which are recurrent events, and severe congestion, the terminal event. Furthermore, a positive correlation between minor and moderate congestion suggests that unobserved factors jointly influence the duration of both. The study confirms the superiority of the proposed joint model for analyzing traffic incident duration and provides practical insights for transportation policymakers to massively ease congestion more effectively.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101209"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145732697","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: 2025-12-25DOI: 10.1016/j.tbs.2025.101219
Zhiyuan Sun , Rui Sun , Zehao Wang , Pengpeng Jiao , Yunxuan Li , Jianyu Wang , Huapu Lu
Individuals’ preferences for connecting transport choice at high-speed rail station have evolved during COVID-19, further resulting in new dynamics after COVID-19. Understanding the shifts in the factors influencing connecting transport choice is vital for effective passenger flow evacuation. However, the influence of most factors is heterogeneous, indicating that these factors exert varying impact under different conditions. This phenomenon presents a challenge in accurately capturing these shifts and developing precise countermeasures designed to promote specific modes of connecting transport. Therefore, this study aimed to investigate the context-dependent effects of factors exhibiting heterogeneity in order to elucidate the underlying causes of heterogeneity and to determine the specific impacts of these factors. Taking Beijing South Railway Station, China, as a case study, two cross-sectional surveys were conducted utilizing an identical questionnaire: one during the pandemic and another following its resolution. Then, an enhanced interpretable machine learning framework based on partially constrained temporal modeling approach was developed to elucidate the context-dependent effects while examining the shifts in these effects. Results show that seven factors marked during COVID-19, as well as fifteen factors marked after COVID-19, were retained by feature selection. Among these factors, paymode, carrying luggage, and distance from the station to the intended destination in Beijing emerged simultaneously during and after the COVID-19 pandemic, indicating that these particular factors emerged as important influences on connecting transport choice than others. Furthermore, it was noted that the effects of six factors demonstrated heterogeneity; specifically, one factor stood out particularly during the COVID-19, while five others were identified after the COVID-19. This suggests that in the post-pandemic era, the influence of various factors on connecting transport choice exhibits distinct characteristics across different conditions.
{"title":"Rethinking the context dependent effects of factors on connecting transport choice at high-speed rail station: Evidence from two cross-sectional surveys during and after COVID-19 pandemic","authors":"Zhiyuan Sun , Rui Sun , Zehao Wang , Pengpeng Jiao , Yunxuan Li , Jianyu Wang , Huapu Lu","doi":"10.1016/j.tbs.2025.101219","DOIUrl":"10.1016/j.tbs.2025.101219","url":null,"abstract":"<div><div>Individuals’ preferences for connecting transport choice at high-speed rail station have evolved during COVID-19, further resulting in new dynamics after COVID-19. Understanding the shifts in the factors influencing connecting transport choice is vital for effective passenger flow evacuation. However, the influence of most factors is heterogeneous, indicating that these factors exert varying impact under different conditions. This phenomenon presents a challenge in accurately capturing these shifts and developing precise countermeasures designed to promote specific modes of connecting transport. Therefore, this study aimed to investigate the context-dependent effects of factors exhibiting heterogeneity in order to elucidate the underlying causes of heterogeneity and to determine the specific impacts of these factors. Taking Beijing South Railway Station, China, as a case study, two cross-sectional surveys were conducted utilizing an identical questionnaire: one during the pandemic and another following its resolution. Then, an enhanced interpretable machine learning framework based on partially constrained temporal modeling approach was developed to elucidate the context-dependent effects while examining the shifts in these effects. Results show that seven factors marked during COVID-19, as well as fifteen factors marked after COVID-19, were retained by feature selection. Among these factors, <em>paymode</em>, <em>carrying luggage</em>, and <em>distance from the station to the intended destination in Beijing</em> emerged simultaneously during and after the COVID-19 pandemic, indicating that these particular factors emerged as important influences on connecting transport choice than others. Furthermore, it was noted that the effects of six factors demonstrated heterogeneity; specifically, one factor stood out particularly during the COVID-19, while five others were identified after the COVID-19. This suggests that in the post-pandemic era, the influence of various factors on connecting transport choice exhibits distinct characteristics across different conditions.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101219"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822770","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: 2025-12-26DOI: 10.1016/j.tbs.2025.101213
Jens Kandt , Hong Deng , Michelle M. Porter
We examine older adults’ leisure activities and mobility needs in one of the most automobile dependent regions in the Global North, the Canadian prairies. The motivation is to identify chances of independent ageing and sustainable travel as the region undergoes demographic transition while being subject to increased risk of extreme weather events that disrupt mobility. Using the Canadian Longitudinal Study on Ageing (CLSA), we identify six groups having different profiles of out-of-home leisure activities that seem to be essential to social connectedness and well-being. We analyse to which extent these practices depend on driving and digital technologies. We identify two groups who show strong signs of transport-related social exclusion. These groups do not benefit from digital technology and are subject to a dual mobility and digital divide that is exacerbated in the context of strong automobile dependence. Policy solutions need to be built on a holistic perspective involving municipal planning, digital skill development, transit funding and adaptation measures. Technological solutions, notably autonomous services, are likely to play a minor role in addressing pressing challenges in the Canadian prairies.
{"title":"Independent ageing, climate risks and automobile dependence in the Canadian prairies: Evidence from the Canadian Longitudinal Study on Aging","authors":"Jens Kandt , Hong Deng , Michelle M. Porter","doi":"10.1016/j.tbs.2025.101213","DOIUrl":"10.1016/j.tbs.2025.101213","url":null,"abstract":"<div><div>We examine older adults’ leisure activities and mobility needs in one of the most automobile dependent regions in the Global North, the Canadian prairies. The motivation is to identify chances of independent ageing and sustainable travel as the region undergoes demographic transition while being subject to increased risk of extreme weather events that disrupt mobility. Using the Canadian Longitudinal Study on Ageing (CLSA), we identify six groups having different profiles of out-of-home leisure activities that seem to be essential to social connectedness and well-being. We analyse to which extent these practices depend on driving and digital technologies. We identify two groups who show strong signs of transport-related social exclusion. These groups do not benefit from digital technology and are subject to a dual mobility and digital divide that is exacerbated in the context of strong automobile dependence. Policy solutions need to be built on a holistic perspective involving municipal planning, digital skill development, transit funding and adaptation measures. Technological solutions, notably autonomous services, are likely to play a minor role in addressing pressing challenges in the Canadian prairies.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101213"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839638","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: 2026-01-13DOI: 10.1016/j.tbs.2026.101234
Matthias Sweet , Darren M. Scott
While many studies lead to expectations of significant post-pandemic telework, has this actually become the reality? Teleworking (work-from-home) may significantly change activity patterns and land markets in metropolitan areas. It stands to gut the downtowns of major metropolitan areas of their knowledge workers. Towards understanding what telework means for future transportation policy, this study explores how teleworking has changed based not only on employees’ expectations of the future, but also based on teleworking rates experienced after the pandemic. Moreover, this study explores what teleworking changes are likely to mean with respect to early fears that downtowns will become hollowed out. Using two waves (2021 and 2023) of the Future Mobility in Canada Survey (FMCS), this study updates estimates of future teleworking using fall 2023 data to reflect on the changing policy implications of teleworking propensities.
Using descriptive statistics and inferential models, this study finds teleworking has lessened between 2021 and 2023, it has decentralized out of the downtowns, it no longer disproportionately offsets transit use, and it is becoming more prominent in households with two or more vehicles. Increasing disconnects between in-person younger workers and virtual older workers portend workplace challenges. Moreover, findings suggest that policymakers will need to wrestle with the question as to whether telework should be viewed as an economic good (notably serving high-income households) or as a merit good (notably serving latent teleworking demand by women). Despite significant uncertainty over the longer-term, significant policy implications appear to hinge on non-transportation outcomes, including time poverty, workplace mentoring, and the meaning of downtowns as work hubs.
{"title":"Interpreting an unfolding future: Is teleworking as common after the pandemic as we expected? and what does it mean for policy?","authors":"Matthias Sweet , Darren M. Scott","doi":"10.1016/j.tbs.2026.101234","DOIUrl":"10.1016/j.tbs.2026.101234","url":null,"abstract":"<div><div>While many studies lead to expectations of significant post-pandemic telework, has this actually become the reality? Teleworking (work-from-home) may significantly change activity patterns and land markets in metropolitan areas. It stands to gut the downtowns of major metropolitan areas of their knowledge workers. Towards understanding what telework means for future transportation policy, this study explores how teleworking has changed based not only on employees’ expectations of the future, but also based on teleworking rates experienced after the pandemic. Moreover, this study explores what teleworking changes are likely to mean with respect to early fears that downtowns will become hollowed out. Using two waves (2021 and 2023) of the Future Mobility in Canada Survey (FMCS), this study updates estimates of future teleworking using fall 2023 data to reflect on the changing policy implications of teleworking propensities.</div><div>Using descriptive statistics and inferential models, this study finds teleworking has lessened between 2021 and 2023, it has decentralized out of the downtowns, it no longer disproportionately offsets transit use, and it is becoming more prominent in households with two or more vehicles. Increasing disconnects between in-person younger workers and virtual older workers portend workplace challenges. Moreover, findings suggest that policymakers will need to wrestle with the question as to whether telework should be viewed as an economic good (notably serving high-income households) or as a merit good (notably serving latent teleworking demand by women). Despite significant uncertainty over the longer-term, significant policy implications appear to hinge on non-transportation outcomes, including time poverty, workplace mentoring, and the meaning of downtowns as work hubs.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101234"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962638","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: 2025-12-10DOI: 10.1016/j.tbs.2025.101202
Hongxia Yuan , Zhongquan Qiu , Han Xu , Renbin Pan , Yusong Yan
Uneven crowding across metro networks, where some lines are severely congested while others remain underutilized, undermines passenger satisfaction and network efficiency. This study examines the potential of coordinated feeder bus services to divert passengers from overcrowded to less crowded lines, using Chengdu, China, as a case study. To assess passenger willingness to switch and capture preference heterogeneity, a stated preference experiment and latent class model were applied under scenarios where the current travel mode was either metro-walking or metro-bus. Two passenger classes were identified. Class A comprised mainly frequent commuters who used the metro at least three days per week. They were highly responsive to service attributes, willing to accept substantially longer metro travel to avoid crowding and transfers, moderate extensions to reduce cost, but only minimal increases in walking or bus access time. Class B, comprising low-frequency, off-peak, and non-commuting passengers, displayed strong inertia and switched only with clear improvements in comfort or walking access. Across scenarios, Class A, particularly metro-bus commuters facing severe crowding and multiple transfers, emerged as the group most likely to switch. Elasticity and sensitivity analyses for Class A further revealed that more severe crowding or transfers on the current route produce stronger switching effects: as crowding intensifies, larger discounts are required to divert passengers to worse alternatives, whereas only modest or no incentives suffice to attract them to better ones. Overall, the findings provide robust evidence that improving feeder bus accessibility can encourage commuters to shift to less crowded metro lines, particularly when combined with service enhancements and targeted incentives.
{"title":"Who is willing to switch to a less-crowded metro route via feeder bus connections? A case study in Chengdu, China","authors":"Hongxia Yuan , Zhongquan Qiu , Han Xu , Renbin Pan , Yusong Yan","doi":"10.1016/j.tbs.2025.101202","DOIUrl":"10.1016/j.tbs.2025.101202","url":null,"abstract":"<div><div>Uneven crowding across metro networks, where some lines are severely congested while others remain underutilized, undermines passenger satisfaction and network efficiency. This study examines the potential of coordinated feeder bus services to divert passengers from overcrowded to less crowded lines, using Chengdu, China, as a case study. To assess passenger willingness to switch and capture preference heterogeneity, a stated preference experiment and latent class model were applied under scenarios where the current travel mode was either metro-walking or metro-bus. Two passenger classes were identified. Class A comprised mainly frequent commuters who used the metro at least three days per week. They were highly responsive to service attributes, willing to accept substantially longer metro travel to avoid crowding and transfers, moderate extensions to reduce cost, but only minimal increases in walking or bus access time. Class B, comprising low-frequency, off-peak, and non-commuting passengers, displayed strong inertia and switched only with clear improvements in comfort or walking access. Across scenarios, Class A, particularly metro-bus commuters facing severe crowding and multiple transfers, emerged as the group most likely to switch. Elasticity and sensitivity analyses for Class A further revealed that more severe crowding or transfers on the current route produce stronger switching effects: as crowding intensifies, larger discounts are required to divert passengers to worse alternatives, whereas only modest or no incentives suffice to attract them to better ones. Overall, the findings provide robust evidence that improving feeder bus accessibility can encourage commuters to shift to less crowded metro lines, particularly when combined with service enhancements and targeted incentives.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101202"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736526","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: 2025-12-03DOI: 10.1016/j.tbs.2025.101208
Meng Guo , Jianing Liu , Sisi Jian , Zheng Li , Gang Ren , Chenyang Wu
As a noteworthy example of subscription-based service in transportation, Mobility-as-a-Service (MaaS) provides seamless and integrated multimodal travel solutions through bundles, encouraging travelers to transition from private modes to sustainable travel options. While previous studies have primarily focused on the impact of MaaS bundles on mode preferences, the complicated and extensive MaaS-induced behavioral changes and their evolving impact have been overlooked. This study addresses this gap by investigating changes in users’ subscriptions and travel choices with accumulated virtual experience of MaaS bundle usage. Combining stated choice experiments and experimental economics, we conduct a four-part multimodal travel experiment targeting commuters, offering an engaging environment where participants make sequential decisions comprising MaaS bundle subscriptions and travel mode choices. Dynamic discrete choice models are formulated to calibrate participants’ dynamic decision-making processes under MaaS bundle subscriptions and behavioral changes over multiple virtual periods. The results indicate that the virtual experience of subscribing to a particular bundle would motivate them to subscribe to the same bundle again in subsequent periods. When MaaS subscribers make mode choices, their behavior is not simply making trade-offs between travel time and cost. Rather, they tend to consider the future use of their bundles fully, and they are more inclined to make travel decisions based on available bundle discounts. The impact of subscriptions is most pronounced in promoting ride-sourcing trips, followed by multimodal and single-mode public transportation options. These findings offer initial insights into the impact of MaaS subscriptions in reshaping traveler’ subscription and travel choices over a relatively longer period.
{"title":"How virtual experience reshapes commuters’ MaaS subscription and mode choice: Insights from an economic experiment","authors":"Meng Guo , Jianing Liu , Sisi Jian , Zheng Li , Gang Ren , Chenyang Wu","doi":"10.1016/j.tbs.2025.101208","DOIUrl":"10.1016/j.tbs.2025.101208","url":null,"abstract":"<div><div>As a noteworthy example of subscription-based service in transportation, Mobility-as-a-Service (MaaS) provides seamless and integrated multimodal travel solutions through bundles, encouraging travelers to transition from private modes to sustainable travel options. While previous studies have primarily focused on the impact of MaaS bundles on mode preferences, the complicated and extensive MaaS-induced behavioral changes and their evolving impact have been overlooked. This study addresses this gap by investigating changes in users’ subscriptions and travel choices with accumulated virtual experience of MaaS bundle usage. Combining stated choice experiments and experimental economics, we conduct a four-part multimodal travel experiment targeting commuters, offering an engaging environment where participants make sequential decisions comprising MaaS bundle subscriptions and travel mode choices. Dynamic discrete choice models are formulated to calibrate participants’ dynamic decision-making processes under MaaS bundle subscriptions and behavioral changes over multiple virtual periods. The results indicate that the virtual experience of subscribing to a particular bundle would motivate them to subscribe to the same bundle again in subsequent periods. When MaaS subscribers make mode choices, their behavior is not simply making trade-offs between travel time and cost. Rather, they tend to consider the future use of their bundles fully, and they are more inclined to make travel decisions based on available bundle discounts. The impact of subscriptions is most pronounced in promoting ride-sourcing trips, followed by multimodal and single-mode public transportation options. These findings offer initial insights into the impact of MaaS subscriptions in reshaping traveler’ subscription and travel choices over a relatively longer period.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101208"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684705","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: 2025-11-17DOI: 10.1016/j.tbs.2025.101179
Tianxin Wang , Guojun Chen , Pengfei Gao , Shuyang Zhang , Li Song
When real-time bus arrival information is provided, passengers adapt their en route behaviour, demonstrating a strong propensity to engage in short-term non-travel activities to enhance travel time utility. However, accurately predicting bus arrivals remains challenging due to various operational uncertainties, necessitating an effective information dissemination strategy for Advanced Traveler Information Systems (ATIS). This study focuses on passengers’ time interval from receiving arrival information to boarding, defined as the “catching process”. To minimize the time cost during this process, we develop a cost-minimization model that treats the disseminated bus arrival time as the decision variable and incorporates passengers’ behaviour feedback, namely, their choice of non-travel activity duration. The theoretical framework determines the optimal dissemination value within the prediction confidence interval. Numerical experiments under a typical scenario show that the optimal dissemination value reduces the expected catching time cost per passenger by an amount equivalent to 5.82 min of in-vehicle time (an 11.5% saving), compared to the benchmark strategy of disseminating the estimated time of arrival. Furthermore, sensitivity analyses reveal that prediction accuracy and travel purpose are critical determinants of the optimal dissemination strategy. In contrast, factors such as bus arrival time distribution, service headway, estimated time of arrival, and passenger access time exhibit negligible influence. These findings indicate that the proposed optimal dissemination strategy is particularly beneficial for ATIS with low prediction accuracy and for passengers with high punctuality requirements.
{"title":"Real-time Bus Arrival Information Service: Optimal Dissemination Value Considering Non-travel Activities","authors":"Tianxin Wang , Guojun Chen , Pengfei Gao , Shuyang Zhang , Li Song","doi":"10.1016/j.tbs.2025.101179","DOIUrl":"10.1016/j.tbs.2025.101179","url":null,"abstract":"<div><div>When real-time bus arrival information is provided, passengers adapt their en route behaviour, demonstrating a strong propensity to engage in short-term non-travel activities to enhance travel time utility. However, accurately predicting bus arrivals remains challenging due to various operational uncertainties, necessitating an effective information dissemination strategy for Advanced Traveler Information Systems (ATIS). This study focuses on passengers’ time interval from receiving arrival information to boarding, defined as the “catching process”. To minimize the time cost during this process, we develop a cost-minimization model that treats the disseminated bus arrival time as the decision variable and incorporates passengers’ behaviour feedback, namely, their choice of non-travel activity duration. The theoretical framework determines the optimal dissemination value within the prediction confidence interval. Numerical experiments under a typical scenario show that the optimal dissemination value reduces the expected catching time cost per passenger by an amount equivalent to 5.82 min of in-vehicle time (an 11.5% saving), compared to the benchmark strategy of disseminating the estimated time of arrival. Furthermore, sensitivity analyses reveal that prediction accuracy and travel purpose are critical determinants of the optimal dissemination strategy. In contrast, factors such as bus arrival time distribution, service headway, estimated time of arrival, and passenger access time exhibit negligible influence. These findings indicate that the proposed optimal dissemination strategy is particularly beneficial for ATIS with low prediction accuracy and for passengers with high punctuality requirements.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101179"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553695","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: 2025-11-07DOI: 10.1016/j.tbs.2025.101170
Cagdas Kara , Asuman S. Turkmen
Selecting the right variables is essential in travel behavior modeling for transportation planning. Traditional methods, like choosing from highly correlated predictors or relying on past studies, can reduce the effectiveness of models. Using robust methods to identify relevant variables helps minimize errors, enhances model understanding, and simplifies future predictions by focusing on key factors, making applications more reliable and efficient.
In this study, the data from the household travel survey within the Eskisehir Transportation Master Plan (conducted in 2001 and 2015) were used for the theoretical modeling. The objective of the study is to develop models for non-home-based travel purposes (e.g., banking, shopping, socializing, visiting, entertainment, recreation, sports, etc.) by incorporating socio-economic demographic parameters and the land-use data to understand the relationships between socio-demographic variables and Other-Purpose Trips (OPT) behavior.
Various theoretical methodologies, including classical Multiple Linear Regression (MLR) in travel models, Ridge Regression, advanced variable selection and machine learning techniques such as Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (ENet), Adaptive Lasso (ALasso), and Adaptive Elastic Net (AEnet) are applied in the study. Ridge Regression and machine learning techniques are implemented to address multicollinearity problem that cannot be handled with the traditional MLR models.
Among the 2001 production models, ENet is approximately 29% more successful than MLR in terms of Cross Validated Root Mean Square Error (CVRMSE). Similarly, ENet demonstrates a 17% higher success rate in predicting the target year (2015) based on Root Mean Squared Error (RMSE). In the 2015 production models, the most successful predictions according to CVRMSE are obtained from AEnet, with a prediction power approximately 45% higher than MLR. Among the 2015 attraction models, AEnet and ALasso, approximately 37% more successful than MLR according to CVRMSE, are found to be the most successful models.
{"title":"Elevating transportation models: A comparative study of variable selection techniques for predictive performance","authors":"Cagdas Kara , Asuman S. Turkmen","doi":"10.1016/j.tbs.2025.101170","DOIUrl":"10.1016/j.tbs.2025.101170","url":null,"abstract":"<div><div>Selecting the right variables is essential in travel behavior modeling for transportation planning. Traditional methods, like choosing from highly correlated predictors or relying on past studies, can reduce the effectiveness of models. Using robust methods to identify relevant variables helps minimize errors, enhances model understanding, and simplifies future predictions by focusing on key factors, making applications more reliable and efficient.</div><div>In this study, the data from the household travel survey within the Eskisehir Transportation Master Plan (conducted in 2001 and 2015) were used for the theoretical modeling. The objective of the study is to develop models for non-home-based travel purposes (e.g., banking, shopping, socializing, visiting, entertainment, recreation, sports, etc.) by incorporating socio-economic demographic parameters and the land-use data to understand the relationships between socio-demographic variables and Other-Purpose Trips (OPT) behavior.</div><div>Various theoretical methodologies, including classical Multiple Linear Regression (MLR) in travel models, Ridge Regression, advanced variable selection and machine learning techniques such as Least Absolute Shrinkage and Selection Operator (Lasso), Elastic Net (ENet), Adaptive Lasso (ALasso), and Adaptive Elastic Net (AEnet) are applied in the study. Ridge Regression and machine learning techniques are implemented to address multicollinearity problem that cannot be handled with the traditional MLR models.</div><div>Among the 2001 production models, ENet is approximately 29% more successful than MLR in terms of Cross Validated Root Mean Square Error (CVRMSE). Similarly, ENet demonstrates a 17% higher success rate in predicting the target year (2015) based on Root Mean Squared Error (RMSE). In the 2015 production models, the most successful predictions according to CVRMSE are obtained from AEnet, with a prediction power approximately 45% higher than MLR. Among the 2015 attraction models, AEnet and ALasso, approximately 37% more successful than MLR according to CVRMSE, are found to be the most successful models.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":"43 ","pages":"Article 101170"},"PeriodicalIF":5.7,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145469083","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号