Travel Behaviour and Society最新文献

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.

Resilience is a characteristic of a system to adapt, resist and recover from disruptions as defined by the U.S. Federal Highway Administration. The concept has been adopted across several fields of research. Existing literature on roadway network resilience typically frames resilience in terms of performance metrics based on the attributes of the network (travel time or travel distance, for example). However, the impact of disruptions to roadway networks varies for different populations within communities because of various socioeconomic factors. While it is important to capture the performance characteristics of transportation networks to ensure goods and services can flow throughout a community, there also lies a need to consider the needs of populations in a community that are more vulnerable to disruptions due to limited mobility. This study aims to propose a framework for roadway network post-disaster recovery planning that considers the needs of socially vulnerable populations. Specific objectives of the study include: i) developing a geographic social vulnerability index (SVI) using census demographic data to quantify the extent to which communities may be considered “socially vulnerable” ii) integrating the index into an actionable decision framework for post-disaster bridge repair strategy and iii) demonstrating how the consideration of social vulnerability can influence network performance. By applying the framework to the Mobile Bay area in Alabama, the significance of including social vulnerability in resilience evaluation becomes evident.

The surge in global electric bicycle ownership has exerted immense pressure on bicycle infrastructure. Theoretically, there’s a need to reassess the risk factors associated with multiple bike lane users. Based on this, there’s a practical need to re-evaluate the safety and quality of outdated infrastructure. This paper aims to reconsider risk factors related to bicycle infrastructure safety in the context of electric bicycles sharing lanes with traditional bicycles. Moreover, many countries lack precise spatial data concerning bicycle infrastructure. This study introduces a mobile sensing method based on bicycles, aiming to acquire daytime and nighttime bike lane datasets in a cost-effective, efficient, and large-scale manner. A computer vision-based bicycle risk factor assessment model was established, and the distribution of bicycle safety risk factors was visually analyzed. Research data was collected from a representative 59.5-kilometer bicycle lane area in Beijing. The results confirm the significant impact of the surge in electric bicycles, with electric bike users accounting for 72.1% of cyclists, 32.3% wearing helmets, and 8.4% riding against traffic. During the day, the highest-ranking risk factors include the type of bicycle lanes (half lacking dedicated lanes or being shared), roadside parking, and subpar road conditions. At night, insufficient street lighting are notable concerns. The research methodology is easily replicable and can be extended to new multi-user coexistence cycling environments or countries without bicycle spatial data, offering insights for bicycle safety policies and road design.

While living in city centers is usually linked to higher accessibility levels, shorter travel times, and higher levels of public transit (PT) utilization, the opposite is true for residents of suburban areas. This assumption holds in metropolitan contexts, where central areas offer better accessibility and are associated with higher levels of PT use. In metropolitan peripheries, a large part of commuting is done on an interurban basis, so that the level of use of public transit can be linked to the supply and information available. This work aims to understand the conditions in terms of transit supply and land use, considering the most frequent trip, psychological variables, and the modal choice of commuters’ motorized modes, in the intention to use the new real-time multi-modal travel app, such as advanced traveler information systems (ATIS) for digital mobility-management assistance. A Structural Equation Model is developed to empirically test a sample of 768 respondents collected in two suburban corridors in the Lisbon Metropolitan Area (LMA). Finally, our findings indicate that residential location, mode choice, and trip complexity have a relevant influence on the intention to adopt travel apps. Male students belonging to Generation Y/Z are the most likely users of travel apps. Regardless of the reason, travel patterns associated with more complex (more transfers) and more frequent trips can reinforce the intention to use apps. Also, it is worth noting that students are frequent public transit users, and public transit is also related to trip complexity.

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.