Travel Behaviour and Society最新文献

All over the world, cities are becoming “smarter.” The improved use of data generated by the transport system (e.g. smart cards data), combined with GPS trackers and citizens’ mobile data enabled the development of a new generation of smarter mobile apps (e.g., Waze) and several local real-time public transport services and trip planners. These technologies have the potential to reduce transport inequalities via two mechanisms. First, governments can develop more efficient and evidence-based public transport plans. Second, citizens have access to better, real-time information about public transport for trip planning and, thus, can make better—i.e., more efficient or cheaper—decisions regarding their everyday mobility. To assess whether citizens’ appropriation of smart mobility—as with their appropriation of any other digital service—depends strongly on having a certain set of socioeconomic and digital attributes, we analyzed both a mobility survey and a digital inequalities survey based on representative samples of adult Uruguayans living in the capital Montevideo. We find that while the use of mobility apps increased in the second half of the 2010s, the adoption of these tools was stratified by traditional digital inequalities, such as type of internet access and digital literacy, and by variables that predict inequalities in both digital and transport fields, such as age, gender and education. As a result, mobility continues to be stratified in favor of those who are more knowledgeable (i.e., tech-savvy) and have greater access to digital technology. Thus, “smart mobility” has failed to ameliorate—and may even exacerbate—transport inequality. Policy implications of these findings are discussed.

While behavioral intention is often considered the immediate predictor of actions, the actual realization of a stated intention is often affected by the intention-behavior gap. This research investigates motorcyclists’ switching intention to an electric moped from a fossil-fuel moped, examining two key components of self-reported intentions: magnitude and uncertainty. Using the judgement uncertainty and magnitude parameters (JUMP) model within the Theory of Planned Behavior framework, we differentiate the two components of stated intention and explore their determinants. Our analysis of data from 293 Taiwanese moped users reveals that while attitude is the major determinant of intention magnitude, intention uncertainty is determined by perceived control, subjective norms and the interaction between attitude and perceived behavioral control. We observed high intention uncertainty across all responses, with negative intentions showing greater uncertainty. Conflicts between attitude and perceived control relate to higher intention uncertainty.

This review explores the potential of digital twin systems to provide a more holistic representation of travel behavior and support transportation planning and policymaking. The paper introduces the concept of digital twins, their key characteristics, and their applications in various domains, including transportation. It discusses the traditional methods used in travel behavior analysis and their limitations, as well as the potential advantages of digital twin systems, such as the integration of heterogeneous data sources, real-time monitoring and prediction, and the ability to simulate and evaluate various policy scenarios. The review also identifies the key components of digital twin systems, the challenges associated with their implementation, and the current state of research on digital twins and related methods in travel behavior analysis. The paper highlights research gaps and future directions, emphasizing the need for privacy-preserving techniques, real-world case studies, and the integration of digital twins with decision support systems. Finally, the review discusses the broader implications of digital twin systems for transportation planning and policymaking, concluding by emphasizing the need for interdisciplinary collaboration and stakeholder engagement to fully realize the potential of digital twins in analyzing travel behavior decisions and shaping the future of transportation systems.

This study aims to extend and investigate how external factors (socioeconomic and demographic characteristics, EV-related policy mechanisms, transportation, and climate conditions) influence the actual adoption of battery electric vehicles (BEVs). Using panel data from 49 U.S. states from 2011 to 2020, we estimate a dynamic spatial Durbin model under the space fixed effect to examine the effects of these attributes on BEV adoption in the neighboring states. The results of the analysis suggest that purchase incentives, the number of public charging stations, gasoline prices, and the Hispanic or Latino population, respectively, have positive total effects on state BEV adoption rates in both the short- and long-term. Particularly, the number of public charging stations has positive direct and indirect effects in both the short- and long-term. Gasoline prices have positive spillover effects in both the short- and long-term. The long-term effects of the three characteristics on BEV adoption are greater than their short-term effects. Based on our findings, this study can provide state officials with practical directions and recommendations to help them allocate their resources and implement timely and appropriate regulations, as well as collaborations between states to increase the penetration rates of zero-emission vehicles (ZEVs).

Promoting the use of sustainable transport alternatives is critical for reducing carbon emissions. In this paper, we propose a cognitive mechanism that explains the extent to which individuals use different sustainable travel modes (e.g., the bus, train, bicycle, and car-sharing). Specifically, we hypothesise negative emotions related to cars as an antecedent of sustainable travel mode use where emotions such as shame, sadness, and upset are positively associated with the extent to which individuals use sustainable transport modes. These negative emotions are further hypothesised to mediate the effect of car attitudes on sustainable travel mode use. Using a broadly representative sample of the UK population (N = 1294), we test these hypotheses and find, firstly, that car attitudes are negatively associated with the use of all sustainable travel modes. Secondly, we demonstrate that negative emotions related to cars mediate this effect. In other words, negative emotions – and not car attitudes – are (positively) associated with the extent to which individuals use all sustainable travel modes. The more individuals perceive the car as something ‘good’, the less they experience emotions such as shame, sadness, and upset when thinking about cars; and it is these negative emotions that then drive sustainable travel mode use. Our study reveals that emotions can and should also be understood as antecedents of sustainable travel modes. We then discuss implications for practitioners and further research.

Concerned by the increasing environmental impact of urban areas and the mobility sector, the study examines mobility in Reykjavík, Iceland. Reykjavík residents have been found to have high emissions in both local and leisure travel. The study aims to explore the connections between urban mobility and leisure travel behaviour using a novel method – activity spaces. The relationship between activity spaces and travel emissions is examined for the first time, based on data of about 700 respondents from a softGIS survey. Connections between activity spaces, local and domestic travel were found. High levels of urban mobility were connected to higher engagement in domestic leisure travel, indicating the presence of a highly mobile lifestyle among Reykjavík urbanites. The reasons for the travel could stem from compensation behaviour, or social network dispersion. In addition, the study points at a lack of functioning 15-minute neighbourhoods and public transit in the capital area. Some differences between income groups were noticed, particularly in urban mobility, pointing at the need to examine the social floor of mobility even within generally affluent societies, so that disadvantaged groups do not get left behind in the sustainable mobility transition. As it has been suggested that people travel for their well-being and to escape the tumult of urban life, the relationship between urban mobility, especially environmental exposure during daily travel, and well-being should be studied. Therefore, the study urges for sustainable mobility transitions and urban planning policies that consider the needs and well-being of citizens in all socio-demographic groups.

The process of urbanization and the rapid development of transit networks transform urban land use properties and individuals’ travel demand, especially in densely populated areas. Traffic surveys reveal that joint activity/travel behaviour constitutes an increasingly significant portion of overall travel behaviours. This paper aims to investigate the spatial–temporal characteristics of individuals’ joint activity/travel patterns (JATPs) within the subway network and explores the factors that influence individuals’ choices among different activity/travel patterns. First, we establish a social relationship classifier to infer acquaintances among individuals. Subsequently, we define four distinct types of JATPs and introduce an algorithm for recognizing these patterns. To implement the proposed framework, we conduct a real-world case study based on subway smart card data from Nanjing, China. Our findings reveal that approximately 19% of daily trips were identified as JATPs, while different JATPs exhibit diverse spatial and temporal characteristics. These differences can be attributed to various influencing factors, including travel/activity time, passenger flow, departure time, and passenger flow/income around tap-in/out stations. In addition, we employ the machine learning classification method XGBoost to predict the discrete choice process of JATP. The classification results reveal the spatial–temporal characteristics of JATP and examine the potential influences of various factors on individuals’ joint activity and travel behaviours. Ultimately, our research sheds light on the evolving landscape of urban travel behaviour in the context of joint activities and travel, with practical implications for transportation planning and policy.

While a substantial body of literature exists on the factors associated with walking behavior, little is known about the long-term effects of childhood experiences. This study utilizes micro-longitudinal data from the Indonesian Family Life Survey (IFLS) to track the lives of 1,343 Indonesians from childhood to adulthood. Findings from a series of regression analyses provide suggestive evidence of the relative importance childhood experiences play in shaping walking behavior during adulthood. Specifically, exposure to a dense urban environment during childhood appears to increase the likelihood of developing sustained walking behavior in later life. Additionally, our findings reveal that individuals raised in households with lower vehicle ownership are more likely to maintain walking behavior during adulthood compared to otherwise similar individuals from households with higher vehicle ownership. In summary, this study highlights the potential long-term efficacy of spatial planning strategies and transportation policies in fostering a more sustainable travel pattern.

Non-mandatory activities (e.g., shopping and leisure) are irregular in space and time, resulting in complex interactions between individuals and urban spaces. Understanding the associated factors of non-mandatory activities is vital for effective urban transport planning and management. This study uses travel survey data from Guangzhou, China, and a random forest (RF) model to investigate non-linear relationships between non-mandatory activities and their associated factors from the perspectives of time, location, built environment, activity dependency, and individual socioeconomic status, on both weekdays and weekends. The contribution of each factor to different non-mandatory activities is examined by a post hoc interpretable method, Shapley Additive exPlanations (SHAP). The results show that activity start time and activity dependency factors have a more significant impact on non-mandatory activities on weekdays, while duration has a greater influence on weekends. Built environment factors like wholesale and retail points of interest (POIs) play a significant role in shopping activities on both weekdays and weekends, while tourism POIs have a greater impact on leisure activities on weekends. Additionally, our analysis reveals the nonlinear dependencies and threshold effects of the top three factors for each category of non-mandatory activities and highlights their disparities between weekdays and weekends.

Travel preferences in public transport (PT) have been substantially affected by the COVID-19 crisis, with rising emphasis on on-board safety and comfort aspects. Hence, real-time crowding information (RTCI) might have become even more instrumental in supporting travel decisions in congested urban PT systems. This study investigates the willingness to wait (WTW) to reduce (or avoid) overcrowding with RTCI in urban PT (bus and tram) journeys, analysing pre- vs. post-COVID travel behaviour attitudes. Stated-preference data and (subsequently estimated) choice models indicate that, while the pre-COVID WTW was primarily driven by mere possibility to avoid an overcrowded first departure, the post-COVID propensity to wait is strongly associated with expectations of seat availability in second departure as well. The ex-post WTW with RTCI seems to have become less-dependent on individual characteristics and more prominent for time-critical (obligatory) trips as well. Our findings underpin the rising relevance of passenger overcrowding in urban PT journeys. Moreover, they help better understand the potential of RTCI in post-pandemic recovery of PT ridership.