Travel Behaviour and Society最新文献

Encouraging residents’ green travel behaviour can reduce carbon emissions. However, existing research focuses more on the individual level than on green travel in the context of group interactions. This study aims to connect individual- and group-level insights by integrating the theory of planned behaviour (TPB) and the norm activation model (NAM). Based on empirical data from a questionnaire survey and web crawler, a simulation of the evolution of green travel behaviour interactions in social networks was conducted. The results reveal that the theory of planned behaviour and the norm activation model (TPB −NAM) can explain and predict green travel behaviour. With individuals’ interactions, social networks present scale-free characteristics, and the state of green travel behaviour tends to be stable. Lowering the opinion-bounded confidence of agents in social networks could promote green travel behaviour. This study extends the literature on the theory of green travel behaviour in terms of model integration and interactive decision-making between individuals and groups. The results support the promotion of residents’ green travel behaviour.

In this paper we assess whether shared e-scooters are being used in hurried situations to avoid arriving late, making trips we define as last-minute trips. We identify trip characteristics for last-minute trips using data from shared e-scooters in Stockholm. Trip arrival time patterns, distances, durations, and speeds are analyzed in relation to desired arrival times. We observe a peak in the number of trips ending before the full hour and a smaller peak of trips ending before the half hour of weekday mornings. These trips have lower average trip durations and higher average speeds, indicating a larger share of last-minute trips. Arrival patterns shift when the assumed preferred arrival time changes, and when the risk associated with arriving late increases. Furthermore, there is an increased share of trips that started close to public transportation around the full hour, indicating that a portion of last-minute trips may also be last-mile trips. Our results show that while last-minute trips are infrequent per user, they do make up a relatively large share of weekday morning trips. These results highlight an important market segment for shared e-scooter services and may also explain why some research reports e-scooter usage replacing walking.

One major challenge of a public transit system is first- and last-mile (FLM) connectivity. With the advent of smart technology and on-demand transportation services, shared micromobility is believed to provide a low-cost solution for bridging the first- and last-mile gap. However, several studies have arrived at mixed conclusions about the FLM achieved by shared mircomobility. This study explores the causal effect of shared dockless e-scooters on last-mile connectivity to public transit by controlling confounding variables, including built environment and socioeconomic status. The study employs a quasi-experimental design. This study defines last-mile connectivity, here, treatment effect, if e-scooter service areas near rail stations increase metro rail ridership after the introduction of e-scooter service. After matching pairs between treatment and control groups using propensity score matching, we use difference-in-difference regression to examine the causal relationship between shared dockless e-scooter service and ridership changes in public rail transit before and after e-scooter service. The findings indicate that the treatment effect, a higher e-scooter trip density around transit buffer areas, positively impacts monthly rail ridership. In conclusion, planning and transportation agencies can develop street design guidelines or transit-oriented development to encourage the active and safe use of e-scooters, thereby promoting better integration of shared dockless e-scooters with transit systems.

Many American cities are testing autonomous shuttles (AS), or self-driving minibuses, as a new mode of public transportation. Existing AS studies often rely on stated preference (SP) surveys that require participants to imagine AS scenarios, or they evaluate short-term pilot programs, thus lacking long-term user insights – particularly in car-dependent suburban contexts. To address these limitations, we conducted a revealed preference (RP) study to identify critical factors influencing AS acceptance among users of Move Nona, the longest-running AS network in the United States, located in Lake Nona, a residential suburb in Orlando, Florida. This study expanded the Technology Acceptance Model (TAM) to include six latent factors: perceived ease of use, perceived usefulness, trust in AS safety, perceived AS reliability under inclement weather conditions, personal innovativeness, and environmental awareness. Our findings indicate that perceived usefulness, ease of use, and environmental awareness positively affect AS acceptance. Contrary to previous research, however, trust and personal innovativeness have minimal impact, suggesting that as users become more familiar with AS, they prioritize essential mobility aspects, such as travel speed and convenience, over technological novelty. These insights are valuable for decision-makers planning to adopt AS to enhance local transit services, while also contributing to the broader field of behavior analysis by corroborating an extended application of TAM in examining AS user acceptance.

The concept of Mobility on Demand (MOD) aims to create a safe and convenient ecosystem for transportation and goods delivery. Despite offering various services, the integration and seamless data exchange among different modes of transportation remain challenging. To address this, research was conducted to examine the current state of integration in the MOD ecosystem, identify gaps in data exchange, and propose policy considerations. The research highlighted the limited integration and inconsistent data structures in the United States (U.S.), attributed to the absence of policy directives and high costs of traditional standards development. Industry-led initiatives are driving progress in standards and specifications. Deep linking and deep integration were distinguished, emphasizing the importance of a truly user-centric MOD solution. Emerging mobility models leveraging blockchain technology show promise in facilitating efficient data sharing and collaboration among various operators. Transportation policy makers play a crucial role in supporting open-source standards and integration efforts. An initial framework utilizing open-source efforts such as Transport Operator Mobility-as-a-service Provider (TOMP), Mobility Data Specification (MDS), General On-demand Feed Specification (GOFS), Transactional Data Specification (TDS), Operational Data Standard (ODS), and City Data Standard for Mobility (CDS-M) was proposed to drive the advancement of scalable and user-centric MOD systems.

The COVID-19 pandemic resulted in drastic changes in people’s living habits. The lockdown, social distancing measures, and the fear of contagion severely impacted the demand for public transportation services. Due to the decline in demand during the pandemic, the financial crisis of the Bus Rapid Transit (BRT) systems in Colombia, similar to other Latin American countries, worsened. This article aims to analyze the pandemic’s impact on Colombia’s seven existing BRT systems by assessing users’ willingness to use the service in a post-pandemic scenario. Data come from an online survey (N = 1,486) in mid-2021, which was used to estimate a hybrid model that assesses future BRT usage intention. Results confirmed that the pandemic had a profound impact on mobility behavior, with a shift away from mass public transportation such as BRT or bus and a substantial increase in the use of single transportation modes, which include cars, motorcycles, cycling, and walking. Intention to continue using BRT service after the pandemic varies between cities and people’s socioeconomic characteristics (i.e. occupation, income, transportation expenditure, and private vehicle ownership). Willingness to use BRT is also influenced by latent variables, including perceptions of contagion risks, service quality before and during the pandemic, and perception of prevention measures implementation. When the virus takes a back seat, the BRT demand is not expected to reach pre-pandemic levels.

Although a large number of studies have found that actual income level significantly affects individuals’ commuting time, from the perspective of bounded rationality, the formation of commuting time is inevitably influenced by individuals’ perceived socioeconomic status. However, there are few empirical studies on how perceived income level, which better measures an individual’s socioeconomic status based on social comparison, affects commuting time. Using a path analysis on a 2020 dataset of Shanghai, China, this study investigates the effect of perceived income level on commuting time, and compares it with the effect of actual income level. Empirical results indicate that actual income level positively impacts perceived income level, and both significantly influence commuting time through the job-housing relationship. However, perceived income level has a negative effect on commuting time, reflecting the matching effect between the perception on socioeconomic status and commuting time. Actual income level has a positive effect on commuting time, supporting the compensation effect between objective income level and commuting time. Nevertheless, under urban development and renewal processes, individuals with lower actual incomes might extend their commuting times and resign themselves to commuting inequality due to the influence of perceived income levels. This study provides a new framework for understanding commuting inequality from an income perspective, and offers insights for optimizing commuting time through urban planning.

E-scooters, a burgeoning form of micromobility, are revolutionizing urban transportation systems globally, particularly in the post-pandemic world. The surge in e-scooter adoption introduces novel regulatory challenges for local authorities. Numerous recent studies cater to these policy needs by exploring e-scooter use. However, scant attention has been paid to non-users’ perspectives, which is crucial to understand comprehensively the effect of e-scooters on urban environments. Issues such as accidents, parking problems, and others, which concern city-dwellers, require close attention. To address this research gap, we used a Q-methodology study to examine the diverse views among residents of the Netherlands. Twenty-one respondents ranked 34 statements pertaining to their disincentives to e-scooter use and elucidated their rankings in a follow-up survey. The study unearthed three distinct viewpoints: (1) “environmentalists” deem e-scooters as environmentally unfriendly, (2) “materialist” exhibit a preference for personal vehicle ownership over shared transport, and (3) “socializers” perceive e-scooters as unsuitable for larger groups. These findings set the stage for potential research directions and underline the importance of incorporating diverse public perceptions in micromobility policymaking.

This study develops a novel theoretical framework to explore the different paths of basic needs and advanced needs on users’ ridesharing intention and ridesharing behavior. Employing partial least squares structural equation modeling and multi-group analysis, questionnaire responses from 1044 users in China were empirically evaluated. The results show that basic needs influence ridesharing intention and ridesharing behavior mediated through psychological factors, while advanced needs directly stimulate ridesharing intention and ridesharing behavior. Factors of basic needs have a greater impact on ridesharing intention and ridesharing behavior than those of advanced needs. Additionally, the effects of the same factor on ridesharing intention and ridesharing behavior vary among users with different genders, ages, incomes, and education levels. Finally, we propose a framework for implementing ridesharing intervention strategies. The aforementioned findings can contribute to the development of specific measures for ride-sourcing platforms and policymakers, thereby playing a significant role in guiding ridesharing at the national level.