Changes In Travel Behavior Research Articles

Internal Factors that Influence Coping in Older Drivers' Transition to Non-Driving.

Quality of life outcomes are associated with the transition to non-driving and depend on effective coping. We examined the relationship between internal factors associated with effective coping and longitudinal changes in travel behavior among drivers aged ≥70 using data from a randomized controlled trial evaluating a driving decision aid. We measured attitudes using the Assessment of Readiness for Mobility Transition (ARMT) and personality using the Ten-Item Personality Inventory (TIPI). We analyzed associations between attitudes and personality with drivers' change in a) alternative transportation use and b) driving behavior over time. Older drivers with high versus low attitudinal readiness for mobility transition were more likely to use alternative transportation over time (adjusted odds ratio = 6.37; 95% confidence interval: 1.45-28.1). We found no association between personality characteristics and alternative transportation use or driving behavior over time. Attitudinal readiness may be a key predictor of effective coping during the transition to non-driving.

Mobility-as-a-service and travel behaviour change: How multimodal bundles reshape our travel choices

Mobility-as-a-service (MaaS) is an innovative framework aiming to promote sustainable mobility via altering users travel behaviour. Despite its potential, empirical evidence of MaaS effectiveness in achieving sustainable goals is scarce due to limited real-world trials and commercial offers of MaaS. This study leverages high-quality data from the Sydney MaaS trial and the Application of Programming Interface (API) to develop an original Error Components-Random Parameter Logit Model that describe the choices of transport modes under the “MaaS era”. Analysis results offer the first insight into how multimodal bundles reshape mode choices and the extent to which subscription bundles could be used as a powerful tool to nudge users towards more sustainable choices. The results show that multimodal bundles present an appealing alternative for the users and help them reduce their private car use. Simulations were conducted to provide guidance for designing multimodal bundles that are both attractive to users and friendly to the environment.

The Role of Technophilia and User Goals in the Intention to Use a Mobility Management Travel App

The ubiquitous use of mobile devices along with the amount of traffic, transportation services, and travel pattern data available has led to the emergence and deployment of smartphone applications for providing information about personal travel management. Several of these travel apps are aimed at voluntary travel behavior change (VTBC) to support and increase sustainable mobility, and have led to the development of research to investigate their influence on travel behavior. Here, the aim is to study the role of technophilia and goal-framing theory in the intention to adopt and situationally use a prospective VTBC travel app. A Structural Equation Model is developed with the aim of empirically testing a sample of 971 respondents collected in two suburban corridors in the Lisbon Metropolitan Area. The results support that goal-framing theory is important for explaining the adoption of VTBC travel apps. Gain and normative motives are more relevant than hedonic motives, pointing to the importance of their tangible benefits. Frequent car users may benefit from VTBC travel apps in terms of encouraging behavioral changes, supporting sustainable mobility management solutions. The results also outline the importance of technophilia and the current use of travel apps in influencing the intention to use VTBC apps.

Disentangling temporal changes in travel behavior: An age-period-cohort analysis based on German travel demand

Travel behavior changes over the temporal dimensions age, period, and cohort. However, longitudinal studies that simultaneously analyze and separate these temporal effects are missing. This study aims to disentangle the temporal changes in travel behavior (participation, frequency, expenses) and explain these changes through different theoretical lenses. Our analysis builds on large-scale representative secondary data from a repeated cross-sectional survey in Germany on the leisure travel behavior between 1983 and 2018 (N = 198,000) and uses generalized additive regression. Age and period are main drivers for changes in travel participation, cohort and age main drivers for changes in travel frequency over time. Relative travel expenses do not substantially change over time. Understanding temporal changes in travel behavior can support long-term planning in tourism.

Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York

The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a “decrease-rebound” trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management.

Understanding the interactions that children and young people have with their natural and built environments: A survey to identify targets for active travel behaviour change in Wales.

Active travel offers many societal benefits, including improving people's mental and physical health and minimising our impacts on the environment. Increasing active travel is particularly important amongst children and young people (CYP), who are building habits which they will carry into adulthood. Studies on active travel amongst CYP are limited, however, with most research focusing on adult participants or on adult perceptions of children. This study sought to understand CYP's interactions with the built and natural environment-and therefore their access to active travel-through the Capability, Opportunity, Motivation, Behaviour (COM-B) model. With a stakeholder group representing local government, youth organisations and active travel organisations, we co-created two bilingual questionnaires-one for young people aged 12-16 years living in Wales and the other for parents of young people aged 12-16 years living in Wales. Both questionnaires collected information on behaviour and perceived capability, opportunity and motivation of CYP to engage with their natural and built environments. The questionnaires included a discrete choice experiment (DCE), which proposed a series of binary choice questions indicating preferences based on landscape, journey time and type of travel. A total of 124 questionnaires (38 young people and 86 parents) were returned for analysis. These data indicate that CYP's time spent outdoors is not dependent upon geography (rural/urban/suburban), season, or school holidays. There was a significant difference in capability, opportunity and motivation between parents and CYP, with parents over-estimating the psychological capability of CYP to engage outdoors. The preference data indicate that active travel is the favoured mode of transport, with both CYP and parents stating that they would increase travel time in order to travel actively. While this response is not consistent with respondent's day-to-day travel choices, it suggests that the limitations to active travel may be psychological capability and automatic motivation, rather than a lack of opportunity.

Influence of telecommuting on out-of-home time use and diversity of locations visited: Evidence from the COVID-19 pandemic

The COVID-19 pandemic has led to unforeseen changes in travel and activity behavior, most notably the wide adoption of telecommuting across various sectors of the workforce. This paper investigates the impact of telecommuting on time use and the number of unique locations visited, both of which have been shown to be closely linked to well-being. Previous telecommuting research often relies on cross-sectional data for which it is difficult to control for unobserved confounding, only analyzes impacts of time-use at the daily level, and has yet to quantify the impact of telecommuting on diversity of locations visited. We use quasi-experimental designs to control for unobserved confounders and extend previous research to identify whether the daily impacts of telecommuting on time-use are additive or substitutional at the weekly level. We use passively collected Point of Interest (POI) data between January 2020 and September 2022, supplemented by five waves of survey responses throughout the COVID-19 pandemic (August 2020, October 2020, December 2020, April 2021, and July 2021) from a panel of U.S. smartphone users. We find that on telecommuting days, workers spend significantly more time at out-of-home non-work locations, estimated to be 114 min prior to the COVID-19 pandemic, decreasing in the early stages of the pandemic to 63 min, and recovering to approximately 120 min in 2022, estimates that are within the range of estimates presented in previous literature. While existing literature focuses on single day analyses, our weekly analysis suggests that daily differences due to telecommuting are substitutional, with the effect of an additional day of telecommuting on time-use at the weekly level being null. Our extension to analyze the impacts of telecommuting on the number of unique locations visited shows that an additional day of telecommuting results in an average decrease of 0.35 in the number of unique weekly locations visited. Collectively, our findings suggest that while telecommuting does not diminish the overall weekly time spent at out-of-home non-work locations, it decreases the diversity of such locations on a weekly level.

Impact of COVID-19 led transition of work culture and travel to work patterns on society and environment in Delhi

Globally, COVID-19 has led to changes in daily lifestyle and travel behavior. There was a shift from in-person physical jobs to work-from-home culture, even for those jobs that required in-person presence at the job location. However, this was not true for everyone, and a majority continued to travel to work. The limited capacity of public transport services and the fear of exposure to the virus restrained people from practicing their conventional mobility choices. We explore the impact of COVID-19 on the nature of jobs and related changes in work travel patterns by population groups for Delhi using in-person surveys conducted during 2021 in six localities of Delhi. We also estimated the short- and long-term impacts of altered behavior on expenditures and equivalent CO2 emissions.As per the analysis, lower income groups, having no vehicles, with lesser qualifications, and employed as daily wagers, had limited capacity to change to work-from-home during COVID-19. We observed an increased dependency on personal motorized vehicles, leading to increased expenditures and reduced affordability. Adopting work-from-home during COVID-19 helped reduce equivalent CO2 emissions per working member by 39 %. However, as the work at the site resumes, the emissions are expected to increase. The study shows that COVID-19 led to short-term benefits, but in the long term, the externalities will likely increase. Working from home during the pandemic provides short-term benefits; the social benefits are not equally distributed, and the transport emissions shall increase in the long term.

Navigating Health-Related Crises: Unraveling the Role of Confidence in Tourism Recovery in Shaping Sustainable Strategies for Tourists’ Intentions across Pandemic Phases

The COVID-19 pandemic has severely impacted global tourism, raising the need for sustainable recovery strategies. This study examines how tourists’ confidence in tourism recovery (CTR) influences travel intentions across different pandemic phases—outbreak, stabilization, and conclusion. Positioned within the Theory of Planned Behavior (TPB), the research explores the mediating role of CTR in the relationships between attitude, subjective norms, perceived behavioral control, and travel intention. Using structural equation modeling (SEM), multi-group analysis (MGA), and Importance–Performance Matrix Analysis (IPMA), this study assesses changes in travel behavior during each pandemic phase. Data were collected through three surveys conducted in major Chinese cities between late 2022 and early 2023. The findings reveal that CTR significantly mediates the influence of behavioral factors on travel intention, especially during the stabilization and conclusion phases. The IPMA results highlight key areas for intervention, with CTR, perceived behavioral control, and subjective norms varying in importance across phases. This research underscores the need for adaptive and sustainable strategies to strengthen traveler confidence, offering practical insights for supporting long-term resilience and growth in the tourism industry during and beyond health-related crises.

Unveiling mobility patterns beyond home/work activities: A topic modeling approach using transit smart card and land-use data

In this paper, a probabilistic topic modeling algorithm called Latent Dirichlet Allocation (LDA) is implemented to infer trip purposes from activity attributes revealed from smart card transit data in an unsupervised manner. While most literature focused on finding patterns for home and work activities, we further investigated non-home and non-work-related activities to detect patterns associated with them. Temporal attributes of activities are extracted from trip information recorded by Tehran subway’s automatic fare collection system. In addition, land-use data is also incorporated to further enhance spatial attributes for non-home/work activities. Various activity attributes such as start time, duration, and frequency in addition to land-use data are used to infer the activity purposes and patterns. We identified 14 different patterns related to non-commuting activities on the basis of both their temporal and spatial attributes including educational, recreational, commercial, and health and other service-related activity types. We investigated passengers’ activity pattern and behavior changes before and during COVID-19 pandemic by comparing the discovered patterns. For recreational patterns it is revealed that not only has the number of recreational patterns dropped, but also the duration of recreational activities decreased. Morning patterns of educational activities have also been eliminated and number of commercial activities was decreased during COVID-19. The proposed model demonstrates the ability to capture travel behavior changes for different disruptions using smart card transit data without performing costly and time consuming manual surveys which can be useful for authorties and decision makers.

Understanding the impacts of COVID-19 on bike-sharing travel behaviors: Insights from the literature and a case study in New York City, USA

The post-coronavirus era is a long-lasting challenge, but also a window of opportunities to strengthen the role of bike-sharing in molding a resilient transportation system. However, few studies have attempted to holistically review the recent literature in the last four years. This study aims to identify emerging topics regarding the impact of COVID-19 on bike-sharing by systematically critiquing research published between 2020 and 2023. The review was complemented by a case study in New York (USA) to address a research gap identified by the review. Scientometric analysis was applied to demonstrate research frontiers. We constructed and visualized network maps that depict information on the citations and terms of the documents that frequently appear in the title, keywords, and abstract of each reviewed document. Next, a clustering-based procedure was developed to infer and compare the major trip purposes in different pandemic periods for the case study. The results reveal that emerging topics include infection risks, active travel, and modal substitution that were less discussed before the pandemic. The results of the case study show that both residential and work trips had experienced significant growth before and after the outbreak, while residential trips saw a slightly higher increase in terms of the number of commuters. Additionally, residential trips can partly replace work trips in most Manhattan communities during the pandemic, implying that people may become more dependent on bike-sharing when working from home is prioritized. This research provides new perspectives on the pandemic’s impact on bike-sharing for researchers. Practitioners may use the tools to better understand how the pandemic may drive changes in travel behaviors and plan accordingly.

Road crash dynamics in Malaysia: Analysis of trends and patterns

Road crashes represent a significant public health and safety concern globally, and Malaysia is no exception. Understanding the trends and patterns of road crashes is essential for devising effective strategies to mitigate risks and enhance road safety. This study presents a comprehensive analysis of road crash dynamics, focusing on road users, severity patterns and geographical patterns in Malaysia from 2012 to 2022. Data sourced from the Royal Malaysian Police (RMP) are utilized to examine various aspects of road crashes. Road crash trend, geographical patterns, linear trend analysis and K-means clustering are employed to explore patterns of road crash in Malaysia. The findings reveal that motorcycles consistently emerged as the most involved road user. Geographical patterns discovered that Selangor exhibits higher crash number. Linear trend analysis revealed significant upward trends in crash frequency prior to the pandemic, while the number of fatalities resulting from road crash showed a downward trend over the observed period. K-means clustering identified that Selangor recorded high total crashes and high total of fatalities. This study also considers the influence of the Covid-19 pandemic on road crash dynamics, highlighting changes in travel patterns and behaviour. There also have been notable successes, such as the reduction in total fatalities and the effectiveness of targeted interventions via the accomplishments of initiatives of Malaysian Road Safety Plan 2014–2022.

Understanding urban traffic flows in response to COVID-19 pandemic with emerging urban big data in Glasgow

Urban traffic analysis has played an important role in urban development, providing insights for urban planning, traffic management, and resource allocation. Meanwhile, the global pandemic of COVID-19 has significantly changed people's travel behaviour in urban areas. This research uses the spatial Durbin model to understand the relationship between traffic flows, urban infrastructure, and socio-demographic indicators before, during, and after pandemic periods. We include factors such as road characteristics, socio-demographics, surrounding built environments (land use and nearby points of interest), and the emerging urban big data source of Google Street View images to understand their influences on time series traffic flows. Taking the city of Glasgow as the case study, we have found that areas with more young and white dwellers are associated with more traffic flows, while natural green spaces are associated with fewer traffic flows. Major roads between cities and towns also show heavier traffic flows. Besides, the application of Google Street View images in this research has revealed the heterogeneous effects of green space on urban traffic flows, as the magnitudes of their effects vary by distance. We also detect that the spatial dependence between adjacent neighbourhoods among the traffic flows and associated urban parameters is variable during the four COVID-19 periods. With the influence of COVID-19, there has been a significant decrease in long-distance travel. The noticeable change in travel behaviour presents a valuable opportunity to encourage active travel in the near future.

Personalizing travel behaviour change interventions using the trans-theoretical model and multimodality data

IntroductionBehaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM.MethodsThis paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices.FindingsThe accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization.ConclusionThe paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available.

Travel behaviour changes among young adults and associated implications for social sustainability

Travel behaviour changes among today’s young adults in relation to important life events, and associated implications for environmental sustainability have been discussed in recent studies. However, the social implications of their travel behaviour changes remain less explored. Applying latent transition analysis on longitudinal data collected in 2019 and 2022, we examined travel behaviour changes among young adults in the Greater Toronto and Hamilton Area, Canada in response to two life events- start of employment after completion of education and residential relocation and explored their social sustainability-related implications in terms of potential transportation-related social exclusion. Young adults showed a higher probability of becoming car users in 2022, however, those who joined workforce and relocated to urban areas were more likely to become transit users and active modes users. Young adults who switched away from active modes to public transit participated in fewer activities and were more likely to experience transportation-related social exclusion. Implications for transportation planning and policy have been discussed.

Travel behaviour transformations in Indonesia: Assessing the long-term impact of COVID-19 on mobility patterns

This study examined the long-term impacts of the COVID-19 pandemic on mobility patterns in Indonesia, focusing on the transition from pre-pandemic norms to the endemic phase. Using quantitative methods and retrospective data analysis, we explored changes in travel behaviour, including mode choice, trip frequency, and travel time. We identified travel behaviour in three phases: pre-pandemic, pandemic (March 2020 to the end of January 2022), and transition to endemic (the data collection period: July – August 2022). Our investigation of the shifts in travel behaviour across these periods enabled us to distinguish between temporary fluctuations and enduring, potentially long-term changes in behaviour. The empirical investigation revealed significant alterations in trip frequency that persisted during the endemic phase, suggesting that these changes in mobility are likely to continue. Factors influencing these shifts include socioeconomic characteristics, work modalities, and attitudes toward health protocols. This study contributes to the understanding of pandemic-induced travel behaviour transformations and provides insights for policymakers and transportation planners to adapt to these changes in the Southeast Asian context.

Acceptance and demand of autonomous vehicles for long-distance recreational travel: An investigation based on a survey of visitors to US national parks

The study of anticipated changes in travel behavior that could be brought by autonomous vehicles (AVs) has been widespread for commuting and short-distance daily travel, yet little attention has been given to long-distance travel. To address this research gap, this paper presents an analytical model for estimating public preference towards the adoption and use (in terms of frequency and length of trips) of AVs, focusing on long-distance recreational travel (LDRT). The model considers socio-demographics, trip-specific characteristics, in-vehicle time use-related factors, and attitudinal latent variables (AV usefulness, AV concern, driving enjoyment) as potential influencers of AV acceptance and demand. To analyze the proposed model, data were collected from a survey of visitors to US national parks conducted in Summer 2022, and the structural equation modeling (SEM) technique was employed. The results indicate that the frequency and length of long-distance recreational trips will likely be higher in the AV era. This draws attention to tourism destination managers not only to manage the tourists’ demand at destinations but also to manage traffic on the roads leading to the destinations. Additionally, the results show that the LDRT demand will continue to rise with the increase in AV acceptance due to the usefulness and potential for several in-vehicle activities offered by vehicle automation, despite concerns about system safety, data privacy, and legal liability. The study also reveals that some travelers will likely miss manual driving enjoyment in AV driving, particularly in unique travel settings like tourism travel, and thus might opt for manual driving options. Based on these findings, we advocate for the timely consideration of induced LDRT, and tourism travel demand generated by AVs—such as provisioning sustainable public transport options to recreational and tourism destinations.

Travel behaviour changes among post-secondary students after COVID-19 pandemic – A case of Greater Toronto and Hamilton Area, Canada

Recent research has reported travel behaviour changes during the COVID-19 pandemic. Speculatively, these short-term disruptions in travel may lead to new habit formation and longer-term travel behaviour changes among young adults belonging to generations Y and Z. Focusing on post-secondary students within Greater Toronto and Hamilton Area, Canada and using longitudinal data collected in fall 2019 and spring 2022, our exploratory study examined the post-COVID-19 travel behaviour changes and analyzed whether these changes are associated with their socio-demographic characteristics and life events experienced over the course of pandemic. Results show that many public transit users and active travellers (pedestrians and cyclists) switched to cars for commuting post-pandemic. The post-pandemic retention of public transit use was lower compared to cars, while active transportation modes had the lowest post-pandemic retention rate. Some socio-demographic characteristics such as age, living situation, work hours and access to cars were significantly associated with these changes. In terms of life events, students who joined workforce after completion of education between 2019 and 2022 were more likely to shift their commute mode from public transit to cars, implying some influence of this life event on commute mode changes, in addition to pandemic-induced changes. Our findings suggest that the post-pandemic commute mode changes observed among young adults in the GTHA may not be a result of only COVID-19 pandemic and may also be partly associated with important life events that they experienced over the course of pandemic. Future transportation planning and policy implications, and directions for future research have been discussed.

Redefining Japan’s Tourism: Post-COVID Challenges and Cultural Dynamics

The global tourism landscape has undergone considerable transformation owing to the COVID-19 pandemic, which has altered its dynamics and created new challenges. Previously, travel was linked to well-being, fostering social connections, and a sense of belonging. However, the pandemic has caused substantial disruptions, leading to job losses and changes in travel behavior. Japan, which was already grappling with overtourism pre-pandemic, faced challenges such as strained infrastructure and environmental impact. Post-pandemic challenges include issues regarding tourist etiquette and labor shortages within the industry. Japan’s deeply rooted mask-wearing culture, driven by social responsibility, may lead to social stigma if norms are ignored, thus affecting tourism. Owing to an aging population and unattractive job profiles, the industry faces labor shortages, which lead to difficulties in recruiting during peak seasons. Therefore, understanding these challenges is crucial for the recovery and revival of tourism in Japan. Strategies must align with evolving societal values and sustainable practices to address behavioral norms and labor shortages. Using tweet-derived data, this study revealed an unexpected increase in discussions regarding mask use, highlighting Japan’s societal emphasis on harmony and consideration of others. This study highlights the challenges faced by tourists and industry professionals, particularly regarding foreign visitors’ comprehension of Japanese cultural norms. The results emphasize the critical factors affecting post-pandemic tourism in Japan, with the aim of exploring cultural influences on sustainable tourism in subsequent research. This endeavor aligns with the vision of the United Nations World Tourism Organization to reconstruct the industry and ensure equitable benefits for all stakeholders.

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.