Current Travel Behavior Research Articles

Potential effects of automated driving on vehicle travel demand: A comparison of three case cities

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%–25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues.

How far are cities from the x-minute city vision? Examining current local travel behavior and land use patterns in Montreal, Canada

In the past three years the 15-minute city planning concept has captured the attention of policy makers and the public worldwide. Some regions have included it in their planning goals and others modified it to 30 min to make it more attainable in their local context. The goal of this research is to measure whether the 15- or 30-minute city goals align with current travel behaviour and land use patterns in the North American context using Montreal, Canada as the case study. In doing so, we look at which destinations are cultivating local travel patterns consistent with the 15- and 30-minute city concepts and identify any unique qualities of these areas using the 2018 Montreal origin-destination travel diaries. Our bivariate analysis finds very few destinations in Montreal where a high number of trips and high percentage of trips are ending using a sustainable mode of transport and below the 15- or 30-minute travel time threshold. We further investigate the land use patterns that align with the 15- and 30-minute city to recommend realistic planning goals and policy interventions that match the North American context. The findings from this research can be of interest to transport professionals and policy makers trying to implement the 15- or 30- minute city concepts to their regions.

Shared versus pooled automated vehicles: Understanding behavioral intentions towards adopting on-demand automated vehicles

The success of future, on-demand AVs depends on whether and how the public adopts them as regular travel modes. To this end, this study has four objectives: (a) understand the effect of current travel behavior on adopting shared automated vehicles (SAVs) and pooled automated vehicles (PooledAVs), and accordingly, propose a theoretical model for behavioral intention to use on-demand AVs based on a mediation analysis; (b) identify the commonalities and differences between the characteristics of SAVs’ and PooledAVs’ early adopters; (c) investigate Canadian residents’ intentions to adopt on-demand AVs; and (d) differentiate between shared mobility-oriented and AV technology-oriented predictors to determine whether behavioral intentions are driven by enthusiasm for AV technology or linked to shared mobility. This study uses data from an online survey administered in fall 2021 to residents of six major Canadian metropolitan areas. Workers and students aged 18 to 75 were selected for analysis (n = 3622). The study employs a full structural equation model (SEM) and a partial mediation analysis. The SEM uses seven affective motivations: attitude towards a behavior, subjective norms, perceived behavioral control, driving enthusiasm, sensation seeking, ample knowledge about AVs, and eco-friendliness. The findings of this paper paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Also, results suggest that although the characteristics of early adopters differ slightly among the examined service types, individuals with positive attitudes who are current users of ride-hailing services are likely to use both on-demand service types.

Multimodal experience as a predictor and catalyst of travel behavior

Three surveys and an intervention experiment among university students investigated the relationship between past experience with multimodal transportation and current travel behavior. The case study university primarily draws students from rural, car-oriented communities but also attracts students from international and domestic urban cities where walking, biking, and public transit are common. Nearly 800 students responded to the first survey. They confirmed the hypothesis that rural students lack experience with multimodal travel and are less likely than their urban classmates to use multimodal opportunities in their new environment. Next, a subset of students was surveyed before and after participating in a multimodal exposure tour (the treatment group). Their responses were compared to students who completed the same surveys but did not participate in the tour. The results indicate that the treatment group increased their use of bus, bike, and walking. The exposure experiment should be interpreted with caution due to a small sample size, nevertheless, the results showed statistical and practical significance and were supported by the initial survey that involved a large, reliable sample. This case study is a microcosm of people moving to new mobility environments and the potential to transform travel habits. The survey design and tour event provide a model for investigating previous travel experience and promoting change in mode choice. The approach of this study can help planners, policymakers, business leaders, and university administrators better understand and shape the travel behavior of their constituents.

Future transport vision propensity segments: A latent class analysis of autonomous taxi market

Autonomous vehicle (AV) is considered a promising mode of transport that has been tested and deployed in various cities worldwide. However, research on how AVs will transform the field of transportation has been hindered by the heavy reliance on attitude measurements and the need for responses from the public who can better represent the future. This study investigates the autonomous taxi market in Huangpu District, Guangzhou, China, where paid and publicly available autonomous taxi service has been in operation for more than a year. This study analyzes 1,158 responses from pioneering and prospective users of autonomous taxi service. Latent class analysis is used to segment autonomous taxi users into classes to reveal the differences in their views on the potential uses of AVs. This study identifies three latent classes: neutral and diverse travelers, conservative and strict travelers, and open and enjoying travelers. Results show that sociodemographic characteristics, household characteristics, and current travel behavior affect the class membership in various means. In addition, users’ perceptions of the benefits and risks of autonomous driving vary across different latent classes. Findings from this study will help improve the quality of autonomous taxi service by enabling customized service based on mobility propensities. This study will also provide insights to help with future vehicle design and transportation policy making.

Investigation of the Policy Effect of High-Speed Metropolitan Rail Service in Seoul Through Latent Class Modeling Analysis of Stated Preference Survey

The sprawl of megacities is increasing the need to improve public transit services to cover longer distances in shorter times than current services. High-speed metropolitan rail services have been introduced to satisfy the needs of passengers and are expected to alleviate the transportation problems caused by urban sprawl. However, the effectiveness of public transit demand inducement policies, which have been employed in various ways in South Korea, has been questioned. Thus, agencies responsible for high-speed metropolitan rail services should also evaluate whether such policies will work properly. The achievement of the policy can be evaluated as sufficiently inducing the demand of the target group, and investigated through the residence information and the current travel behavior. This study investigates the current travel behavior of users to be replaced when a new high-speed metropolitan rail service is introduced and directly analyzes the travel mode shifting effect. While previous studies focused on increasing the applicability to general services by classifying groups based on personal attributes, this study focuses on evaluating detailed policy achievements for specific services. We apply a latent class modeling approach using stated preference survey data for group classification. In particular, by employing the user’s current travel behavior as a class membership attribute, the characteristics of potential passengers of the high-speed metropolitan rail service are analyzed. The estimation results suggest that current travel behavior significantly classifies high-speed rail passengers. In addition, the policy achievement was investigated in detail by region and operation time, and several additional policy directions are presented to satisfy the policy goals.

Examining the Disruptive Potential of Generation Z Tourists on the Travel Industry in the Digital Age

The tourism industry has faced several challenges over the years, due to the evolution of technology and behavioral changes of the generations. The research focused on the new generation of tourists, Generation Z. Known as digital natives, the study aimed to identify their current travel behavior in the digital age and their perception of the future of travel in the context of recent technological developments, namely artificial intelligence, and virtual reality, thus highlighting specific elements that could disrupt the travel industry. To achieve this goal, qualitative research was conducted, using two sessions of focus groups among 20 Generation Z tourists. The results show that Generation Z tourists exhibit disruptive behavior primarily due to their heavy reliance on social media platforms, even for travel purposes. Social media has become their primary search engine, and travel influencers hold significant sway over certain individuals in this generation. In addition, they serve as influencers, by sharing visual content from their travels. Another noteworthy trend in the travel industry is Generation Z’s inclination towards multi-channel booking, effortlessly switching between different booking options. Lastly, although the results show little awareness of the potential of advanced technologies, their openness to adopt them to simplify the travel planning process further contributes to the disruption of traditional travel patterns. Generation Z can be considered a bridge between previous and future generations. The study has implications for management and marketing activities in the tourism field.

How does transport supply and mobility behaviour impact preferences for MaaS bundles? A multi-city approach

Mobility-as-a-Service (MaaS) bundle design has recently gained increasing attention. Previous work has identified socio-demographics and current travel behaviour as drivers towards adopting MaaS bundles. Yet, the focus has been on (scientific) trials in one specific area or on one specific service. We extend this work by analysing the influence of transport supply and mobility behaviour on preferences for MaaS bundles in multiple cities. To this end, we conduct a stated preference experiment in 83 cities in Germany. Respondents choose between two MaaS bundle options and one pay-as-you-go option. Besides public transport, we include shared e-scooters and bikes or carsharing and ridepooling in the bundles. To integrate city characteristics we merge this data with supply data from the shared services and data about public transport quality in the respective cities. We find previous shared mobility usage to positively and car usage to negatively influence bundle uptake. While included units are crucial for bundles integrating car-based shared modes, additional prices beyond these units are more important for shared micro-mobility services. The quality of the local transport system and shared mobility supply is an important precondition for MaaS bundle uptake.

Attitude changes, modelling travel behaviour, and ex ante project evaluations

The literature has increasingly recognized that people’s attitudes can change for many reasons. This paper argues that attitude changes can have important implications for modelling travel behaviour and ex ante evaluations of candidate policy options. Limiting ourselves to the transport system and the built environment, we discuss why attitudes could change, how these changes are not addressed in current aggregate travel behaviour models, mainly the four step model, and how they could influence the assessment of candidate policy options. We suggest related avenues for future research. The core point we want to make is that attitude changes are not included in aggregate models, which leads to an underestimation of the impacts (often: benefits) of unconventional transport policy options.

Examining factors influencing the adoption of solo, pooling and autonomous ride-hailing services in Australia

While ride-hailing has become ubiquitous in most metropolitan areas, it is still unclear how it prevails in the regional areas, especially after the emergence of new services of pooling or self-driving rides. This paper contributes to this knowledge gap by examining the regional/metropolitan dimension in the factors influencing the frequency of ride-hailing use and the demand for pooled and autonomous vehicle (AV) rides in Australia. Through individual-level survey data, we found that there is indeed a divide between factors affecting not only the frequency of using ride-hailing in the status quo but also in the future adoption of new services of pooled and AV-based services. Using the hybrid discrete choice model, we identify several latent psychological constructs that influence the use of ride-hailing services, namely being pro-technology, pro-pooling, pro-AV, anti-driving and security-cautious traits. Moreover, the results of multivariate models point to different observed and latent characteristics influencing the adoption of pooled and AV ride-hailing services for various trip purposes. In line with the literature, it was found that the socio-demographic characteristics that affect the frequency of use or adopting pooled and AV services in future include gender, age, income, education attainment, employment status, car ownership, and the current travel behaviour. We also found that ride-hailing in Australia has played more as a substitution role for public transport, private car and taxi than as a complementary role or feeder of the public transport system, both in the metropolitan and regional areas.

Exploring user requirements and service features of e-micromobility in five European cities

This research was specifically aiming to understand the current travel behavior of individuals in five different locations related to e-micromobility. The basis of the analysis was a well-designed survey built to extract objective trip information when using e-micromobility, and subjective information of individual perspectives towards the service. The survey was mainly focusing on service usage, customer satisfaction, and trip combinations, with a specific focus on knowledge about regulations. In general, travelers are familiar with e-micromobile services and regulations, but they primarily use them for leisure trips as short-term renting, not on a regular basis. It seems that most users would shift from walking and public transport modes. The main benefits of e-micromobility are its flexibility and speed, while the concerns cover potential conflicts with other road users, safety issues, and incorrect parking of e-micromobility vehicles. Comparing and analyzing the results among the chosen cities helped understanding the strengths and weaknesses of e-micromobility, as well as the potential of new mobility services. We also aimed to analyze future implications, which supports long-term policy making and potential effects on the transportation network and city structure.

Impacts of subjective evaluations and inertia from existing travel modes on adoption of autonomous mobility-on-demand

As autonomous vehicle (AV) technology advances, it is important to understand its potential demand and user characteristics. Literature from stated preference surveys find that attitudes and current travel behavior are as or more important than demographics in determining intention to purchase or use AVs. Yet to date no study has looked at how attitudes and use of existing modes both simultaneously affect AV adoption. In this study, we conduct a stated preference survey in Singapore to investigate how the subjective evaluation of existing travel modes (attitudes) and inertia based on previous use of existing modes affect the adoption of an autonomous mobility-on-demand service (AMOD). Using a sample size of 2,003 individuals and 11,613 choice observations, we estimate a mixed logit discrete choice model incorporating latent variables capturing subjective evaluations of existing travel modes (determined through confirmatory factor analysis), a two-part formulation of modal inertia, and other trip-specific and socio-demographic variables. Results show that subjective evaluation and use of existing modes both affect the adoption of AMOD. Specifically, people with a positive evaluation of ridehailing and those who are current ridehailing users are more likely to choose AMOD. Additionally, those who are current car drivers are more likely to choose AMOD, while users of public transit were less likely to choose AMOD. Given that ridehailing is the closest existing mode to our hypothetical AMOD service, our results might suggest that how AVs are implemented and their similarity to existing modes may be critical to the formation of attitudes and direction of inertia impacting adoption. Our research provides insights on the potential relationship between AVs and existing modes that could valuable in AV network design and service planning.

Are multimodal travellers going to abandon sustainable travel for L3 automated vehicles?

Reducing car dependency supports the creation of a more sustainable transport system. However, automated vehicles (AVs) are predicted to increase the attractiveness of car travel and decrease the use of public transport and active travel. This current study explored how travellers’ intention to use AVs and their current travel behaviour influence their expectations of how they will use public transport and active travel, once conditionally automated (SAE L3) vehicles (L3 AVs) are available.Survey data (collected during the EU H2020 L3Pilot project) from among current car users from eight European countries (n = 9118) was used. Respondents were asked about their current travel mode usage, intention to use L3 AVs, and expected changes in the use of public transport and active travel once L3 AVs are available. The respondents were divided into nine user segments based on their level of intention to use L3 AVs and multimodality.Most respondents did not foresee changes in their use of public transport (62%) or active travel (67%). A higher intention to use L3 AVs increased the probability of a traveller expecting to decrease their use of public transport and, to a lesser extent, active travel. Multimodal travellers used public transport and active travel regularly and were also more likely to see a change, either up or down, in their use of public transport and active travel. The results suggest that L3 AVs may pose a challenge to the sustainability by encouraging current users of public transport and active travel to switch to personal AVs.

Exploring Preferences for Transportation Modes in the City of Munich after the Recent Incorporation of Ride-Hailing Companies

The growth of ride-hailing (RH) companies over the past few years has affected urban mobility in numerous ways. Despite widespread claims about the benefits of such services, limited research has been conducted on the topic. This paper assesses the willingness of Munich transportation users to pay for RH services. Realizing the difficulty of obtaining data directly from RH companies, a stated preference survey was designed. The dataset includes responses from 500 commuters. Sociodemographic attributes, current travel behavior and transportation mode preference in an 8 km trip scenario using RH service and its similar modes (auto and transit), were collected. A multinomial logit model was used to estimate the time and cost coefficients for using RH services across income groups, which was then used to estimate the value of time (VOT) for RH. The model results indicate RH services’ popularity among those aged 18–39, larger households and households with fewer autos. Higher income groups are also willing to pay more for using RH services. To examine the impact of RH services on modal split in the city of Munich, we incorporated RH as a new mode into an existing nested logit mode choice model using an incremental logit. Travel time, travel cost and VOT were used as measures for the choice commuters make when choosing between RH and its closest mode, metro. A total of 20 scenarios were evaluated at four different congestion levels and four price levels to reflect the demand in response to acceptable costs and time tradeoffs.

Investigating heterogeneity in preferences for Mobility-as-a-Service plans through a latent class choice model

The past decade has seen the introduction and widespread availability of a number of new mobility services. These have created a transport environment that is complex to navigate for passengers. The Mobility as a Service (MaaS) concept aims to provide a solution, by offering a single digital interface through which users can plan journeys, pay for and access a variety of transport modes. MaaS can also provide users with various products, including pay-per-use access to transport modes as well as MaaS packages. The latter are bundled mobility services that combine a variety of transport modes and are offered to customers in a one-stop-shop manner. The objective of this paper is to examine individual preferences for MaaS packages, specifically addressing the question of preference heterogeneity. In doing so, a Latent Class Choice Model (LCCM) is developed, allowing us to reveal variations in individuals’ preferences. The LCCM is estimated using data from a MaaS-related market research carried out in Greater Manchester. The results imply significant heterogeneity with regards to preferences. Three latent classes emerged through the analysis, all with different MaaS package preferences and individual characteristics. Age, gender, income, education and current travel behaviour all play an important role in determining an individual’s propensity to purchase MaaS packages. The results can provide valuable insights into the types of people that should and should not be initially targeted with MaaS packages to maximise uptake.

Impact of childhood experiences on public transport travel behaviour

Mobility policies and planning need evidence to target individuals effectively in moving to more sustainable transport behaviour outcomes where car use is reduced, and multimodal public transport use is enhanced. Building on more recent travel behaviour research which has a socio-psychological approach and the mobility biography literature, this paper focusses on an individual’s public transport travel behaviour, as impacted by travel and residential location experiences in their childhood. The empirical analysis uses a bespoke retrospective survey with respondents from the Greater Metropolitan area of Sydney in New South Wales, Australia where understanding better the motivations underpinning travel behaviour are particularly acute, given the car dependent nature of Australian cities. A path model illustrates how residential neighbourhood perceptions and travel attitudes impact on travel behaviour and how these are influenced by background, including childhood residential and travel experiences. The results show the dominance of current travel attitudes on current travel behaviour but childhood experience is also significant and appearing to work by impacting current residential neighbourhood perceptions and travel attitudes, suggesting travel behaviour not only affected by personal attitudes and socio-demographic characteristics, but also influenced by context (spatial, social, cultural, etc.). When travel behaviour is seen as a social construct influenced by long standing and entrenched experiences influencing current perceptions and attitudes, policy that identifies which mobility practices are likely to be flexible and open to change is more likely to be successful in changing behaviour.

Spatial mobility and current travel behaviour in the metropolitan hinterland

Spatial mobility and current travel behaviour in the metropolitan hinterland

On sceptics and enthusiasts: What are the expectations towards self-driving cars?

Automation in transport is increasing rapidly. While it is assumed that automated driving will have a significant impact on travel demand, the nature of this impact is not clear yet. Based on an online survey (N = 3040), this study explores the expected consequences of automated driving in the Danish population. Participants were divided into three homogeneous segments based on attitudes towards automated and conventional car driving: Sceptics (38%); Indifferent stressed drivers (37%) and Enthusiasts (25%). The attitudinal segments differ in their socio-demographic profiles, current travel behaviour, interest in use-cases for self-driving cars, and anticipated changes of behaviour in a future with self-driving cars. People who are enthusiastic about self-driving cars are typically male, young, highly educated, and live in large urban areas, while Sceptics are older, car reliant and more often live in less densely populated areas. The indifferent group consists of more car reluctant people. The expected advantages of self-driving cars generally resemble the aspects highlighted in other studies, such as relief from driving tasks and the possibility of doing other things while travelling, with some variation between the three segments. Preferred future scenarios include car ownership rather than sharing solutions as well as residential relocation, which is considered by 22% of all participants as a consequence of the possibility of working in the car (13% of Sceptics; 28% of Enthusiasts). All in all, increased travel demand can be expected from an uptake of increasingly automated cars, which will be realised in the different segments with different speeds, depending on policies, business models, and proven functionality and safety.

Cycling environmental perception in Beijing – A study of residents' attitudes towards future cycling and car purchasing

This study focuses on three groups: cyclists, non-cyclists, and non-car owners and examines the significance of the perceived cycling environment, current travel behavior, urban form and socio-demographic variables for the respondents' attitudes towards future cycling and car purchasing. The paper uses survey data (N = 1427) collected in eight Beijing neighborhoods. The analysis is carried out by means of principal component analysis and multinomial logistic regression analysis. The respondents were generally more positive towards continuing cycling or cycling more in the future than towards car purchasing. The perceived cycling environment was found to be associated with respondents' attitude towards future cycling and car purchasing. The higher the level of satisfaction with the clarity of cycling space allocation and the higher the agreement with pro-cycling policies, the higher the probability that the respondents will cycle in the future and the lower probability that they will buy a car. Associations with current travel behavior indicate that long everyday travel distances (e.g. 10 km to work or longer) negatively affects the respondents attitude towards their future cycling, whereas short everyday travel distances up to 2 km are positively linked to future cycling prospects. Non-car owners' attitude to future car ownership is strongly linked to socio-demographic status - low education and low income level groups seems to be most unlikely to take up driving in the future. To encourage people to cycle more and drive less, policy should direct efforts to promoting the clarity of cycling space on the street and strengthen pro-cycling policies. Attention should also be given to stabilizing the current travel modes of non-car users, including promoting the image of cycling, improving the service of walking, cycling, public transport and generally by introducing attractive alternatives to private car ownership.

Predicting discordance between perceived and estimated walk and bike times among university faculty, staff, and students

ABSTRACTEvidence indicates the importance of walking and biking as a form of transportation, known as active travel (AT), on various health outcomes. Time is one of the most cited barriers to AT. The objective of this study was to examine which variables predict walk and bike discordance (discrepancy between perceived an actual travel time) to campus among faculty, staff, and students. This was a cross-sectional online survey conducted in the Fall of 2014 with a volunteer sample of university students (n = 252) and faculty and staff (n = 253) at a large northeastern university. Participants responded to questions on demographics, psychosocial, AT, AT correlates, and residence. Google maps were used to determine time and distance from their residence to their most frequented campus location, from which discordance was determined. Among faculty and staff, bike discordance was associated with gender, age, biking to/from campus, and AT. Walk discordance was associated with walking to/from campus and distance to campus. Among students, bike discordance was associated with having a parking permit, self-efficacy for biking, gender, age, biking to/from campus, and AT. Walk discordance was associated with having a parking permit and distance to campus. Among faculty and staff, current travel behavior and factors relative to more AT behaviors are important predictors of discordance. Similar results were found among students. Individuals who AT more often likely have more concordance due to behavioral exposure. Likewise, those who are more fit are more likely to be health conscious and thus more likely to incorporate physical activity into other aspects of life.HighlightsTime is one of the most cited barriers to active travel.Variables associated with discordance (discrepancy between actual travel time and perceived travel time) were determined.A cross-sectional online survey with a sample of university students, and faculty, and staff at a large northeastern university.Travel behavior and factors related to more AT behaviors are important predictors of discordance.Individuals who AT more often and are more fit likely have more concordance due to behavioral exposure.