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A Mobility-as-a-Service (MaaS) concept, UbiGo, was implemented in Gothenburg, Sweden, and used for a 6-month period by 195 individuals in 83 households. Four participant subgroups were identified: Car shedders, Car accessors, Simplifiers, and Economizers. A qualitative analysis revealed that the subgroups had different reasons to join the service and different expectations of the change that would occur on the basis of the altered preconditions offered by the service. Previous car users reduced their use of private car and increased their use of public transport and active modes. Participants who did not have access to a privately-owned car but thought they needed one discovered that they managed well without. Other participants were reinforced in their existing behaviors but in ways they did not envisage, depending on which goals they had at the outset of the trial. Overall, the participants were also satisfied with the service, as well as with stated changes and non-changes, even if this in some cases meant more planning. Based on the empirical findings it could be argued that a service approach, such as UbiGo, has the potential to reduce the need for private car ownership, and enable people to change their mode choices and travel patterns. The potential relies however on a number of specific features of the service of which flexibility and a need- rather than a mode-based approach are key features.

Over the past decades research on travel mode choice has evolved from work that is informed by utility theory, examining the effects of objective determinants, to studies incorporating more subjective variables such as habits and attitudes. Recently, the way people perceive their travel has been analyzed with transportation-oriented scales of subjective well-being, and particularly the satisfaction with travel scale. However, studies analyzing the link between travel mode choice (i.e., decision utility) and travel satisfaction (i.e., experienced utility) are limited. In this paper we will focus on the relation between mode choice and travel satisfaction for leisure trips (with travel-related attitudes and the built environment as explanatory variables) of study participants in urban and suburban neighborhoods in the city of Ghent, Belgium. It is shown that the built environment and travel-related attitudes—both important explanatory variables of travel mode choice—and mode choice itself affect travel satisfaction. Public transit users perceive their travel most negatively, while active travel results in the highest levels of travel satisfaction. Surprisingly, suburban dwellers perceive their travel more positively than urban dwellers, for all travel modes.

Automated driving technologies are currently penetrating the market, and the coming fully autonomous cars will have far-reaching, yet largely unknown, implications. A critical unknown is the impact on traveler behavior, which in turn impacts sustainability, the economy, and wellbeing. Most behavioral studies, to date, either focus on safety and human factors (driving simulators; test beds), assume travel behavior implications (microsimulators; network analysis), or ask about hypothetical scenarios that are unfamiliar to the subjects (stated preference studies). Here we present a different approach, which is to use a naturalistic experiment to project people into a world of self-driving cars. We mimic potential life with a privately-owned self-driving vehicle by providing 60 h of free chauffeur service for each participating household for use within a 7-day period. We seek to understand the changes in travel behavior as the subjects adjust their travel and activities during the chauffeur week when, as in a self-driving vehicle, they are explicitly relieved of the driving task. In this first pilot application, our sample consisted of 13 subjects from the San Francisco Bay area, drawn from three cohorts: millennials, families, and retirees. We tracked each subject’s travel for 3 weeks (the chauffeur week, 1 week before and 1 week after) and conducted surveys and interviews. During the chauffeur week, we observed sizable increases in vehicle-miles traveled and number of trips, with a more pronounced increase in trips made in the evening and for longer distances and a substantial proportion of “zero-occupancy” vehicle-miles traveled.

This study provides a better understand the mechanism underlying the built environment-behavior connection by systematically exploring the relationships between the objective (actual) environment and people’s perceptions of the environment, and their relative effects on travel behavior using the Stimuli-Organism-Response framework. Based on data for the Twin Cities, this study explores (1) How do perceptions mediate the effects of the objective environment on travel behavior? (2) How do travel attitudes influence the effects of perceptions on travel behavior? Among the eight empirical models tested here, six are consistent with the framework: objective built environment affects travel behavior through its influence on perceptions. Moreover, the framework fits walking and bicycling behavior better than transit and driving behavior. Furthermore, travel attitudes greatly moderate the influences of perceptions on travel behavior.

Using Herfindahl–Hirschman Index and the Mobidrive and Thurgau six-week travel diary datasets this paper examines the degree of repetition of individuals’ choices of their daily activity–travel–location combinations. The results show that the repetitiveness of individual activity–travel–mode–location combinations is highly influenced by the individuals’ out-of-home commitments, the intra-household conditions and the availability and the accessibility of the activity locations. Different types of activity have different pattern of repetition. The level of repetition of individual’s daily activity–travel pattern is less correlated to travel mode choice, but more to the individuals’ commitments and obligations. The repetitiveness of mode choices is more related to the conditions or the accessibilities of the activity location, but not directly to the activity itself.

The widespread adoption of smartphones followed by an emergence of transportation network companies (TNC) have influenced the way individuals travel. The authors use the 2017 National Household Travel Survey to explore socioeconomic, frequency of use, and spatial characteristics associated with TNC users. The results indicate that TNC riders tend to be younger, earn higher incomes, have higher levels of education, and are more likely to reside in urban areas compared to the aggregate United States population. Of the TNC users, 60% hailed a ride three times or less in the previous month, indicating that TNC services are primarily used for special occasions. TNC users use public transit at higher rates and own fewer vehicles compared to the aggregate United States population. In fact, the TNC user population reported similar frequencies of use for both TNC services and public transit during the previous month. Approximately 40% of TNC users reside in regions with population densities greater than 10, 000 persons per square mile compared to only 15% for non-TNC users. Lastly, reported use of public transit for TNC users living in large cities (> 1 million) with access to heavy rail was almost three times greater when compared to similar sized cities without heavy rail. The average monthly frequency of TNC use was also elevated when heavy rail was present.

The influence of the built environment on travel behavior has been the subject of considerable research attention in recent decades. Scholars have debated the role of residential self-selection in explaining the associations between the built environment and travel behavior. The purpose of this study is to make a contribution to the literature by adopting the cross-lagged panel modeling approach to analyze a panel data, which scholars have recommended as the ideal design for studying the influence of the built environment on travel behavior accounting for the residential self-selection. To that objective, we collected activity-travel diary data from a sample of 229 households in Beijing before and after they moved from one residential location to another. We developed a two-wave structural equation model linking the residential built environment to travel behavior and taking into consideration travel-related attitudes before and after residential change. The modeling results show that individuals’ travel attitudes may change after a home relocation. We found no evidence of residential self-selection, but significant influence of the built environment on travel preference. Nevertheless, the direct influence of travel preference on travel behavior seems to be stronger than that of the built environment. As one of the very few studies to use panel data, this research presents new insights into the relationship between the built environment and travel behavior and the role of residential self-selection.

Much of the literature shows that a compact city with well-mixed land use tends to produce lower vehicle miles traveled (VMT), and consequently lower energy consumption and less emissions. However, a significant portion of the literature indicates that the built environment only generates some minor—if any—influence on travel behavior. Through the literature review, we identify four major methodological problems that may have resulted in these conflicting conclusions: self-selection, spatial autocorrelation, inter-trip dependency, and geographic scale. Various approaches have been developed to resolve each of these issues separately, but few efforts have been made to reexamine the built environment-travel behavior relationship by considering these methodological issues simultaneously. The objective of this paper is twofold: (1) to better understand the existing methodological gaps, and (2) to reexamine the effects of built-environment factors on transportation by employing a framework that incorporates recently developed methodological approaches. Using the Seattle metropolitan region as our study area, the 2006 Household Activity Survey and the 2005 parcel and building data are used in our analysis. The research employs Bayesian hierarchical models with built-environment factors measured at different geographic scales. Spatial random effects based on a conditional autoregressive specification are incorporated in the hierarchical model framework to account for spatial contiguity among Traffic Analysis Zones. Our findings indicate that land use factors have highly significant effects on VMT even after controlling for travel attitude and spatial autocorrelation. In addition, our analyses suggest that some of these effects may translate into different empirical results depending on geographic scales and tour types.

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.

BackgroundThe explosive growth of social media has rendered them powerful communication channels. User generated content is an important source of inspiration and influence among web friends, it generates new activities and consequently affects mobility decisions. Whether to visit a place, or how to get to a place of interest are decisions that can be triggered through people’s interactions on social media.ObjectiveThe main objective of this paper is to investigate the influence of social media use on activity planning and travel arrangements for women and men.MethodsAn online survey was conducted to examine the social media use and the impact of the shared content for women and men, on the phase before any activity in an urban environment. Inferential statistics were applied to detect gender differences in a sample size comprised of 804 respondents.ResultThe significant results showed that the variables gender and social media use for activity planning and travel arrangements are associated with each other. Results have also indicated that the influence of reviews and ratings, photos/ videos and proposed transport mode on activity planning is gender dependent. Photos/ videos influence more often both women (m=3.47) and men (m=3.00) than reviews and ratings (m=3.21 for women and 2.94 for men). Both these contents influence more than proposed transport mode (m=2.62 and 2.37 for women and men).ConclusionThe analysis of the results indicated that before an activity, both women and men tend to use majorly social media for activity planning and travel arrangements, while photos/videos influence women’s decisions more often than men.Travel arrangements of the majority of respondents would be influenced by a post of a designated account related to transport. Finally, social media use affects travel arrangements of both women and men more before performing an activity rather than during.