Pub Date : 2024-10-12DOI: 10.1016/j.tbs.2024.100922
Traffic congestion has imposed considerable economic expenses and environmental challenges on metropolitan areas. Consequently, cities have implemented Travel Demand Management (TDM) strategies to mitigate this issue during peak hours. Although studies have investigated how individuals make decisions during commuting in response to TDM incentives, there is limited research on differences in route choices between trips to and from work, making the policies less effective. This study aims to fill this gap by using trajectory data from over 3,000 vehicles and examines the impacts of time-varying features, route characteristics, and built environment factors on route variability. Results indicate that factors such as expressway proportion, travel cost, and road density at the origin and destination locations have similar effects on route variability during morning and evening commuting. However, departure time, travel distance, and the number of traffic lights significantly differ in impacting route variability between the two scenarios. This study provides a foundation for optimizing route choices and alleviating traffic emissions during peak hours through advanced TDM measures. With more detailed and deliberate policies, citizens can enjoy urban mobility within a well-organized road network in a more sustainable and efficient way.
{"title":"Heterogeneity in route choice during peak hours: Implications on travel demand management","authors":"","doi":"10.1016/j.tbs.2024.100922","DOIUrl":"10.1016/j.tbs.2024.100922","url":null,"abstract":"<div><div>Traffic congestion has imposed considerable economic expenses and environmental challenges on metropolitan areas. Consequently, cities have implemented Travel Demand Management (TDM) strategies to mitigate this issue during peak hours. Although studies have investigated how individuals make decisions during commuting in response to TDM incentives, there is limited research on differences in route choices between trips to and from work, making the policies less effective. This study aims to fill this gap by using trajectory data from over 3,000 vehicles and examines the impacts of time-varying features, route characteristics, and built environment factors on route variability. Results indicate that factors such as expressway proportion, travel cost, and road density at the origin and destination locations have similar effects on route variability during morning and evening commuting. However, departure time, travel distance, and the number of traffic lights significantly differ in impacting route variability between the two scenarios. This study provides a foundation for optimizing route choices and alleviating traffic emissions during peak hours through advanced TDM measures. With more detailed and deliberate policies, citizens can enjoy urban mobility within a well-organized road network in a more sustainable and efficient way.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420389","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 : 2024-10-11DOI: 10.1016/j.tbs.2024.100928
Time-use surveys provide useful data for travel analyses. However, the survey on time use and leisure activities (TULA) Questionnaire A, a representative time-use survey in Japan, does not include questions related to the locations of activities, thus making it difficult to use for travel analyses. This study proposes machine-learning methods to determine the in-home/out-of-home situations of TULA Questionnaire A using TULA Questionnaire B with activity locations as the training data. Random forest performs better than logistic regression and decision trees in the inference. The activity was the most important factor in determining the in-home/out-of-home situations, followed by the accompanying person and time of day. The inferred outputs in the TULA Questionnaire A included the individual-based out-of-home rate profiles and the proportions of mobile persons from 1996 to 2016. Using these outputs, we analyzed trip misreporting in household travel surveys. Comparisons with nationwide and Tokyo person trip (PT) surveys implied soft refusals and trip misreporting in travel surveys. The comparison with the nationwide PT surveys suggested higher soft refusals on weekends than on weekdays. The comparison with the 1998, 2008, and 2018 Tokyo PT surveys implied the increased soft refusal in PT surveys, particularly among the male group aged between 20 and 39 and the female group aged between 35 and 49 during 1998–2018. These results suggest that careful handling of recent household travel survey data may be required. In addition, the proposed machine-learning-based method enables us to utilize the rich sample of Questionnaire A for activity-based travel analysis in future studies.
时间利用调查为旅行分析提供了有用的数据。然而，作为日本具有代表性的时间利用调查，时间利用和休闲活动调查（TULA）问卷 A 并不包括与活动地点相关的问题，因此难以用于旅行分析。本研究提出了机器学习方法，使用带有活动地点作为训练数据的 TULA 问卷 B 来确定 TULA 问卷 A 的在家/外出情况。在推理中，随机森林的表现优于逻辑回归和决策树。活动是决定在家/不在家情况的最重要因素，其次是陪同人员和时间。TULA 问卷 A 的推断输出包括 1996 年至 2016 年基于个人的外出率概况和流动人员比例。利用这些输出结果，我们分析了家庭旅行调查中的旅行误报情况。与全国和东京个人旅行（PT）调查的比较意味着旅行调查中的软拒绝和旅行误报。与全国范围的 PT 调查相比，周末的软拒绝率高于工作日。与 1998 年、2008 年和 2018 年东京公共交通调查的比较表明，在 1998 年至 2018 年期间，公共交通调查中的软拒绝现象有所增加，尤其是在 20 岁至 39 岁的男性群体和 35 岁至 49 岁的女性群体中。这些结果表明，可能需要谨慎处理近期的家庭旅行调查数据。此外，建议的基于机器学习的方法使我们能够在未来的研究中利用问卷 A 的丰富样本进行基于活动的旅行分析。
{"title":"Inferring in-home/out-of-home situations unreported in time-use surveys using supervised machine learning","authors":"","doi":"10.1016/j.tbs.2024.100928","DOIUrl":"10.1016/j.tbs.2024.100928","url":null,"abstract":"<div><div>Time-use surveys provide useful data for travel analyses. However, the survey on time use and leisure activities (TULA) Questionnaire A, a representative time-use survey in Japan, does not include questions related to the locations of activities, thus making it difficult to use for travel analyses. This study proposes machine-learning methods to determine the in-home/out-of-home situations of TULA Questionnaire A using TULA Questionnaire B with activity locations as the training data. Random forest performs better than logistic regression and decision trees in the inference. The activity was the most important factor in determining the in-home/out-of-home situations, followed by the accompanying person and time of day. The inferred outputs in the TULA Questionnaire A included the individual-based out-of-home rate profiles and the proportions of mobile persons from 1996 to 2016. Using these outputs, we analyzed trip misreporting in household travel surveys. Comparisons with nationwide and Tokyo person trip (PT) surveys implied soft refusals and trip misreporting in travel surveys. The comparison with the nationwide PT surveys suggested higher soft refusals on weekends than on weekdays. The comparison with the 1998, 2008, and 2018 Tokyo PT surveys implied the increased soft refusal in PT surveys, particularly among the male group aged between 20 and 39 and the female group aged between 35 and 49 during 1998–2018. These results suggest that careful handling of recent household travel survey data may be required. In addition, the proposed machine-learning-based method enables us to utilize the rich sample of Questionnaire A for activity-based travel analysis in future studies.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.tbs.2024.100923
This study investigates the impact of a newly implemented public transport interchange discount policy in Suzhou, China, focusing on its effects on metro-to-bus interchange behaviors across various spatial and temporal dimensions. Utilizing two distinct datasets spanning periods before and after the policy’s implementation, a comprehensive spatial–temporal analysis was conducted, covering weekdays, weekends, and holidays. A novel, real-time, distance-weighted methodology was developed to more accurately identify metro-to-bus interchange catchments, thereby refining the modeling scope. The study examines the interplay between land use, socio-demographic factors, and bus-related attributes—including a newly proposed operation-opportunity combined bus accessibility metric—using an explainable machine learning approach. Results indicate that the interchange discount policy has had an overall positive, though varied, impact on interchange behaviors, with the most pronounced effects observed during weekdays in central urban areas and at metro line bends. Specifically, 76.1 % of metro stations saw an increase in metro-to-bus interchange ratios on weekdays following the policy’s implementation, with increases observed at 66.4 % and 67.3 % of stations during weekends and holidays, respectively. Overall, the interchange ratio increased by 12.49 %, with a 17.45 % increase on weekdays. The analysis also reveals that factors such as bus accessibility, bus-to-bus interchange, and population density exhibit different effects depending on the time of week, with non-linear patterns emerging. The policy’s introduction shifted the impact thresholds for certain factors, initially triggering competition between bus and metro services but eventually leading to a synergistic rise in metro-to-bus transfers as bus-to-bus interchange ratios increased. Additionally, the policy altered the significance of population density, enhancing the attractiveness of multimodal interchange for users who previously favored other modes of transport.
{"title":"Free interchange for better transit? Assessing the multi-dimensional impacts on metro to bus interchange behavior − insights from an explainable machine learning method","authors":"","doi":"10.1016/j.tbs.2024.100923","DOIUrl":"10.1016/j.tbs.2024.100923","url":null,"abstract":"<div><div>This study investigates the impact of a newly implemented public transport interchange discount policy in Suzhou, China, focusing on its effects on metro-to-bus interchange behaviors across various spatial and temporal dimensions. Utilizing two distinct datasets spanning periods before and after the policy’s implementation, a comprehensive spatial–temporal analysis was conducted, covering weekdays, weekends, and holidays. A novel, real-time, distance-weighted methodology was developed to more accurately identify metro-to-bus interchange catchments, thereby refining the modeling scope. The study examines the interplay between land use, socio-demographic factors, and bus-related attributes—including a newly proposed operation-opportunity combined bus accessibility metric—using an explainable machine learning approach. Results indicate that the interchange discount policy has had an overall positive, though varied, impact on interchange behaviors, with the most pronounced effects observed during weekdays in central urban areas and at metro line bends. Specifically, 76.1 % of metro stations saw an increase in metro-to-bus interchange ratios on weekdays following the policy’s implementation, with increases observed at 66.4 % and 67.3 % of stations during weekends and holidays, respectively. Overall, the interchange ratio increased by 12.49 %, with a 17.45 % increase on weekdays. The analysis also reveals that factors such as bus accessibility, bus-to-bus interchange, and population density exhibit different effects depending on the time of week, with non-linear patterns emerging. The policy’s introduction shifted the impact thresholds for certain factors, initially triggering competition between bus and metro services but eventually leading to a synergistic rise in metro-to-bus transfers as bus-to-bus interchange ratios increased. Additionally, the policy altered the significance of population density, enhancing the attractiveness of multimodal interchange for users who previously favored other modes of transport.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420388","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 : 2024-10-11DOI: 10.1016/j.tbs.2024.100927
Bike-sharing systems play a crucial role in encouraging sustainable transportation, and understanding their usage characteristics is essential for enhancing their contribution to urban mobility. This research seeks to investigate how weather conditions impact the utilization of a small-scale docked bike-sharing system. The study employed Generalized Linear Mixed Effects (GLME) models to analyze interactive events, using categorized weather parameters to represent various weather conditions. Several models were developed to comprehensively understand distinct travel behaviors and identify significant weather variables affecting the frequency of bike trips for transportation and leisure purposes. The findings reveal that rain had a significant deterrent effect on leisure cycling, particularly on weekdays. Cold and hot weather conditions exhibited a more pronounced impact on weekday bike trips, while weekend bike trips appeared to be less influenced by weather variables. The fall season was found to be the least favorable for leisure trips, while winter was determined to be the most unfavorable for transportation trips. Furthermore, hot days in the summer season negatively impacted bike usage only on weekdays. These insights have important implications for the development of a more resilient bike-sharing system, particularly in small-scale contexts. They provide valuable recommendations for tailored strategies to mitigate the impact of adverse weather conditions, thereby fostering an increase in usage of bike-sharing systems for both leisure and transportation purposes.
{"title":"Assessment of weather-driven travel behavior on a small-scale docked bike-sharing system usage","authors":"","doi":"10.1016/j.tbs.2024.100927","DOIUrl":"10.1016/j.tbs.2024.100927","url":null,"abstract":"<div><div>Bike-sharing systems play a crucial role in encouraging sustainable transportation, and understanding their usage characteristics is essential for enhancing their contribution to urban mobility. This research seeks to investigate how weather conditions impact the utilization of a small-scale docked bike-sharing system. The study employed Generalized Linear Mixed Effects (GLME) models to analyze interactive events, using categorized weather parameters to represent various weather conditions. Several models were developed to comprehensively understand distinct travel behaviors and identify significant weather variables affecting the frequency of bike trips for transportation and leisure purposes. The findings reveal that rain had a significant deterrent effect on leisure cycling, particularly on weekdays. Cold and hot weather conditions exhibited a more pronounced impact on weekday bike trips, while weekend bike trips appeared to be less influenced by weather variables. The fall season was found to be the least favorable for leisure trips, while winter was determined to be the most unfavorable for transportation trips. Furthermore, hot days in the summer season negatively impacted bike usage only on weekdays. These insights have important implications for the development of a more resilient bike-sharing system, particularly in small-scale contexts. They provide valuable recommendations for tailored strategies to mitigate the impact of adverse weather conditions, thereby fostering an increase in usage of bike-sharing systems for both leisure and transportation purposes.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420387","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 : 2024-10-11DOI: 10.1016/j.tbs.2024.100919
Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.
{"title":"Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons","authors":"","doi":"10.1016/j.tbs.2024.100919","DOIUrl":"10.1016/j.tbs.2024.100919","url":null,"abstract":"<div><div>Bike-riding GPS data offers detailed insights and individual-level mobility information which are critical for understanding bike-riding travel behaviour, enhancing transportation safety and equity, and developing models to estimate bike route choice and volumes at high spatio-temporal resolution. Yet, large-scale bicycling-specific GPS data collection studies are infrequent, with many existing studies lacking robust spatial and/or temporal coverage, or have been influenced by sampling biases leading to these data lacking representativeness. Additionally, accurately detecting bike-riding trips from continuously collected raw GPS data without human intervention remains a challenge. This study presents a novel GPS data collection approach by leveraging the combination of a smartphone application with a Bluetooth beacon attached to a participant’s bike. Aided by minimal heuristic post-processing, our method limits data collection to trips taken by bike without the need for participant intervention, carefully optimising between survey participation, privacy challenges, participant workload, and robust bike-riding trip detection. Our method is applied to collect 19,782 bike trips from 673 adults spanning eight months and three seasons in Greater Melbourne, Australia. The collected dataset is shown to represent the underlying adult bike-riding population in terms of demographics (sex, occupation and employment type), temporal and spatial patterns. The average trip length (median = 4.8 km), duration (median = 20.9 min), and frequency of bicycling trips (median = 2.7 trips/week) were greater among men, middle-aged and older adults. The ‘Interested but Concerned’ riders (classified using Geller typology) rode more frequently, while the ‘Strong and Fearless’ and ‘Enthused and Confident’ groups rode greater distances and for longer. Participants rode on roads/streets without bike infrastructure for more than half of their trips by distance, while spending 24% and 17% on off-road paths and bike lanes respectively. This population-representative dataset will be key in the context of urban planning and policymaking.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-09DOI: 10.1016/j.tbs.2024.100900
This paper presents a novel methodology for assessing emergency response capabilities in coastal cities in China amidst the challenges posed by global warming, rapid tourism industry growth, and increasing flood occurrences. Our approach integrates flood simulation, traffic big data, and web-based path navigation to evaluate the emergency response of the Fire & Rescue Service (FRS) to tourist hotels in Shanghai. The empirical study highlights the significant impact of transportation conditions, hotel locations, flood inundation intensity, and urban FRS distribution on emergency response effectiveness. It further demonstrates that existing traffic conditions heavily influence flood-induced emergency accessibility, with severe congestion adversely affecting spatial accessibility. The study also reveals that flooding events and real-time traffic can cause delays in emergency responses by altering optimal routes. Consequently, selecting the most efficient routes becomes crucial for enhancing a city’s emergency response capabilities. The results validate the efficacy of our proposed approach, which holds significant promise for improving emergency response capabilities in urban tourism settings when faced with disasters.
{"title":"The emergency accessibility analysis based on traffic big data and flood scenario simulation in the context of Shanghai hotel industry","authors":"","doi":"10.1016/j.tbs.2024.100900","DOIUrl":"10.1016/j.tbs.2024.100900","url":null,"abstract":"<div><div>This paper presents a novel methodology for assessing emergency response capabilities in coastal cities in China amidst the challenges posed by global warming, rapid tourism industry growth, and increasing flood occurrences. Our approach integrates flood simulation, traffic big data, and web-based path navigation to evaluate the emergency response of the Fire & Rescue Service (FRS) to tourist hotels in Shanghai. The empirical study highlights the significant impact of transportation conditions, hotel locations, flood inundation intensity, and urban FRS distribution on emergency response effectiveness. It further demonstrates that existing traffic conditions heavily influence flood-induced emergency accessibility, with severe congestion adversely affecting spatial accessibility. The study also reveals that flooding events and real-time traffic can cause delays in emergency responses by altering optimal routes. Consequently, selecting the most efficient routes becomes crucial for enhancing a city’s emergency response capabilities. The results validate the efficacy of our proposed approach, which holds significant promise for improving emergency response capabilities in urban tourism settings when faced with disasters.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420383","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 : 2024-10-09DOI: 10.1016/j.tbs.2024.100920
The planning ethos of providing proximity-based services to all inhabitants has been prevailing recently, and underlines the importance of knowing areal differences in collective activity space (AS) of populations. Mobile phone signaling data (MSD) has great potentials for this end, especially in mega-cities with fast changing and spatially varying demographic composition. However, two problems need to be addressed when applying MSD-based AS measurement for planning practices, including the identification of regularly visited locations and the selection of measure indices. This paper proposes a three-step workflow to apply the MSD to measure local collective AS with considering addressing the problems. This three-step workflow aims to illustrate the procedure of using MSD to measure collective AS for supporting planning practice in urban China, with clarifying some key concerns when doing so. We apply the workflow to examine the spatial heterogeneity of the collective AS in Shenzhen City and discuss the transferability of the workflow in different social and institutional contexts.
为所有居民提供就近服务的规划理念近来十分盛行，这也凸显了了解人口集体活动空间（AS）的区域差异的重要性。移动电话信令数据（MSD）在这方面具有巨大的潜力，尤其是在人口构成快速变化、空间差异较大的特大城市。然而，在规划实践中应用基于 MSD 的活动空间测量时，需要解决两个问题，包括识别经常访问的地点和选择测量指数。本文在考虑解决这些问题的基础上，提出了应用 MSD 测量地方集体 AS 的三步工作流程。该三步工作流程旨在说明使用 MSD 测量集体自适应状态以支持中国城市规划实践的程序，并澄清测量过程中的一些关键问题。我们运用该工作流程考察了深圳市集体自治的空间异质性，并讨论了该工作流程在不同社会和制度背景下的可移植性。
{"title":"Exploring collective activity space and its spatial heterogeneity using mobile phone signaling Data: A case of Shenzhen, China","authors":"","doi":"10.1016/j.tbs.2024.100920","DOIUrl":"10.1016/j.tbs.2024.100920","url":null,"abstract":"<div><div>The planning ethos of providing proximity-based services to all inhabitants has been prevailing recently, and underlines the importance of knowing areal differences in collective activity space (AS) of populations. Mobile phone signaling data (MSD) has great potentials for this end, especially in mega-cities with fast changing and spatially varying demographic composition. However, two problems need to be addressed when applying MSD-based AS measurement for planning practices, including the identification of regularly visited locations and the selection of measure indices. This paper proposes a three-step workflow to apply the MSD to measure local collective AS with considering addressing the problems. This three-step workflow aims to illustrate the procedure of using MSD to measure collective AS for supporting planning practice in urban China, with clarifying some key concerns when doing so. We apply the workflow to examine the spatial heterogeneity of the collective AS in Shenzhen City and discuss the transferability of the workflow in different social and institutional contexts.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420384","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 : 2024-10-08DOI: 10.1016/j.tbs.2024.100921
Autonomous vehicles (AVs) can potentially revolutionize the transportation system, but the extent of their impact may depend on users’ attitude and AV-related policies. This paper seeks to provide a holistic view of the impacts of policy, attitudinal, and sociodemographic factors on AV adoption intention. An extension to the original Technology Acceptance Model is proposed by incorporating perceived enjoyment (i.e., how enjoyable respondents think using an AV will be) and policy factors. Four policy factors include the availability of financial incentives, awareness campaigns, traffic policies, and legislative measures. Using 1,831 survey responses in China, multiple linear regression models were estimated to quantify the direct impacts of the proposed policy and attitudinal factors on AV adoption intention. They also illustrate the moderating effects of these policies on the relationships between attitudinal factors and AV adoption intention. The study findings may be used to design future policy measures to facilitate a smooth transition to an era of AVs.
{"title":"Moderating effects of policy measures on intention to adopt autonomous vehicles: Evidence from China","authors":"","doi":"10.1016/j.tbs.2024.100921","DOIUrl":"10.1016/j.tbs.2024.100921","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) can potentially revolutionize the transportation system, but the extent of their impact may depend on users’ attitude and AV-related policies. This paper seeks to provide a holistic view of the impacts of policy, attitudinal, and sociodemographic factors on AV adoption intention. An extension to the original Technology Acceptance Model is proposed by incorporating perceived enjoyment (i.e., how enjoyable respondents think using an AV will be) and policy factors. Four policy factors include the availability of financial incentives, awareness campaigns, traffic policies, and legislative measures. Using 1,831 survey responses in China, multiple linear regression models were estimated to quantify the direct impacts of the proposed policy and attitudinal factors on AV adoption intention. They also illustrate the moderating effects of these policies on the relationships between attitudinal factors and AV adoption intention. The study findings may be used to design future policy measures to facilitate a smooth transition to an era of AVs.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420378","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 : 2024-10-07DOI: 10.1016/j.tbs.2024.100924
Increasing the share of travelers using public transport is one way to address environmental problems such as carbon dioxide emissions. Senior citizens represent an increasingly important group in this transition, as they are increasingly mobile and make up a large share of the population. In this paper, we investigate senior citizen’s mobility decision-making, focusing on public transport. Through a survey of 5,000 people in three metropolitan areas in Sweden, we find that while senior citizens desire to live in harmony with nature, they are less likely to see car traffic as a cause of environmental problems. They also struggle with the development of digital service delivery options of public transport. For instance, they use apps less, and like using timetables on paper and signs at bus stops more than younger public transport users. Even so, they are more satisfied with public transport than younger travelers, indicating that many seniors like using public transport, despite lacking the environmental motivations to do so.
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Pub Date : 2024-10-04DOI: 10.1016/j.tbs.2024.100918
Active Mobility Devices (AMDs) such as electric scooters (e-scooters) and electric bikes (e-bikes) are increasingly used on shared paths and Park Connector Networks (PCNs) in Singapore, leading to frequent interactions between AMD riders, pedestrians, and cyclists. To ensure safety, it is crucial to understand the factors associated with collision risk related to these AMDs. To gain insights into the riders’ perspectives on the risk-taking behaviours and attitudes towards safety and sharing paths, a survey was conducted with 369 e-bike and 133 e-scooter riders across Singapore. The collected data was analysed to identify critical features of behaviour, attitudes, and opinions of e-scooters/e-bikes riders and their impact on perceived collision risk. Logistic Regression was used to select the most important behavioural features linked to collision risk, and significance of each was quantified by using the odds ratios in the chosen model. The results reveal that e-bike riders who regularly brake hard to avoid obstacles and highly value capacity of e-bike to carry goods face an increase in collision risk by 49.1% and 43.48% respectively. Those preferring quieter AMDs face 33.31% lower collision risk. Additionally, e-bike riders advocating for more traffic enforcement or the importance of slowing down when overtaking pedestrians face 20.69% and 38.84% lower collision risk respectively. E-scooter riders who manoeuvre quickly to dodge collisions or prioritize passenger-carrying capacity encounter a 142.25% and 67.43% higher collision risk, respectively. Furthermore, e-scooter riders willing to bend rules when not causing inconvenience to others face an increase in collision risk by 123.00%. These outcomes offer significant insights for the design and regulation of active mobility to safeguard all road users in a multi-modal transport environment.
{"title":"Impact of attitude, behaviour and opinion of e-scooter and e-bike riders on collision risk in Singapore","authors":"","doi":"10.1016/j.tbs.2024.100918","DOIUrl":"10.1016/j.tbs.2024.100918","url":null,"abstract":"<div><div>Active Mobility Devices (AMDs) such as electric scooters (e-scooters) and electric bikes (e-bikes) are increasingly used on shared paths and Park Connector Networks (PCNs) in Singapore, leading to frequent interactions between AMD riders, pedestrians, and cyclists. To ensure safety, it is crucial to understand the factors associated with collision risk related to these AMDs. To gain insights into the riders’ perspectives on the risk-taking behaviours and attitudes towards safety and sharing paths, a survey was conducted with 369 e-bike and 133 e-scooter riders across Singapore. The collected data was analysed to identify critical features of behaviour, attitudes, and opinions of e-scooters/e-bikes riders and their impact on perceived collision risk. Logistic Regression was used to select the most important behavioural features linked to collision risk, and significance of each was quantified by using the odds ratios in the chosen model. The results reveal that e-bike riders who regularly brake hard to avoid obstacles and highly value capacity of e-bike to carry goods face an increase in collision risk by 49.1% and 43.48% respectively. Those preferring quieter AMDs face 33.31% lower collision risk. Additionally, e-bike riders advocating for more traffic enforcement or the importance of slowing down when overtaking pedestrians face 20.69% and 38.84% lower collision risk respectively. E-scooter riders who manoeuvre quickly to dodge collisions or prioritize passenger-carrying capacity encounter a 142.25% and 67.43% higher collision risk, respectively. Furthermore, e-scooter riders willing to bend rules when not causing inconvenience to others face an increase in collision risk by 123.00%. These outcomes offer significant insights for the design and regulation of active mobility to safeguard all road users in a multi-modal transport environment.</div></div>","PeriodicalId":51534,"journal":{"name":"Travel Behaviour and Society","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420375","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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