Travel Behaviour and Society最新文献

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.

Causal inference with the difference-in-differences (DID) framework is popular in identifying causal effects with observational data and has started to be applied in recent travel behaviour studies. Most relevant transportation research adopts the conventional linear parametric DID model, which is known to be inflexible and restrictive. This study applies non-parametric DID estimators facilitated by machine learning (ML) models for causal inference in a variety of data scenarios. Semi-parametric and doubly robust estimators are established and integrated with the ML-based cross-fitting pipeline. Simulation studies and empirical case studies are conducted to showcase the ability of ML-based DID to detect causal effects from both simulated and real-world datasets. Results suggest that the proposed methods outperform conventional DID models in all data scenarios. Light working models are generally preferred over hyperparameter-dependent ones for their comparable performance, lower computational burden, and higher levels of compatibility to real-world empirical analysis. Empirical case studies also demonstrate how the proposed DID method could be applied to evaluate the impacts of various interventions on travel behaviour in different contexts. The present study adds to the existing travel behaviour literature by leveraging machine learning algorithms and non-parametric estimators to the impact evaluation of external interventions on travel characteristics and expanding the application of causal inference approaches in transportation research.

Addressing air travel demand is a key open challenge in transitioning to a low-carbon society. In Switzerland, where most flights are from/to nearby European countries, their substitution with night train services is a promising alternative. However, still little research investigates whether the demand for night trains is well-established and the motivations behind it. We aim to bridge this gap through a mixed-method study involving 389 Swiss residents who travelled by night train in 2022/23. A web survey was first conducted to identify latent classes of participants based on their daily and long-distance travel mode choices and evaluate relevant predictors of class membership. Participants of each class were then randomly selected to be involved in an in-depth interview on personal experiences and motivations underlying their choices. Results identify three classes of travellers distinguished by a predominant use of rail and public transport (greens), mixed long-distance travel mode choices and car-oriented daily mobility (pragmatists), preference for flying combined with public transport and active daily mobility (dissonants). Travel cost reduction is the only motivation significantly differentiating pragmatists and dissonants from the greens. This result suggests that fluctuations in ticket prices are the main driving force in shifting demand from flights to night trains.

Customer citizenship behavior (CCB) refers to actions that provide value to companies, such as offering feedback, being patient with disruptions, and assisting other customers. This is especially important in the airline industry, where services are provided collectively in a shared environment. Recognizing that the factors influencing CCB might differ based on national culture is crucial, particularly for the international operations of the airline industry. However, the impact of national culture on shaping CCB in this industry remains unclear. Using three theoretical lenses, namely expectation confirmation theory (ECT), social exchange theory (SET), and cultural dimensions theory (CDT), this study aims to investigate how national culture affects the formation of CCB through the proposed conceptual model. To achieve this, the study gathered responses from 743 passengers—both Turkish (n = 323) and British (n = 420)—using a structured web-based questionnaire. The analysis employed partial least squares structural equation modeling (PLS-SEM) and multigroup analysis (MGA). The results indicated that factors like service quality and value for money have a positive influence on customer satisfaction. Customer satisfaction has a positive impact on behaviors such as giving feedback, advocating for the company, assisting others, and being patient with disruptions. The proposed relationships are also mediated by value for money and customer satisfaction. This study stands as the first effort to investigate how national culture moderates the formation of CCB in the context of the airline industry.

Not only in geography is motorcycle transport still a very under-researched phenomenon. Although there has been a recent growth in academic interest in motorcycle transport, there are still some key questions that remain unanswered. Empirical studies analysing motorcycle transport are surprisingly also based mainly on observations in the Global South, with very little research in the economically developed countries of the Global North. This paper aims to examine the spatial patterns of motorcycle transport in the Czech Republic. For the analysis, we use official data on average motorcycle intensities on the road network (i), and unofficial data crowdsourced from individual GPS navigations and social networks (ii). We found major inconsistencies between the two data sources. The paper’s main contribution is to demonstrate the great potential of crowdsourced data to identify important aspects of the spatial organisation of motorcycle traffic. Based on these data, we found substantial differences in the intensity of motorcycle traffic on the road network (different use of individual categories of communications according to both types of data), the popularity of individual motorcycle routes (higher popularity of scenic roads in GPS data), and the different lengths of individual trips during weekdays and weekends.

Understanding the relationship between the emerging shared mobility and the metro is essential for their successful integration. Although several studies have examined specific shared mobility modes individually, the differences between these modes in terms of integration with the metro system have largely been neglected. We address this research gap by investigating the impact of bike-sharing and ride-hailing on metro ridership, using a comprehensive dataset collected in Shenzhen. We also conduct a comparative analysis of these two shared mobility modes based on temporal and spatial dimensions and proximity to job centers. Our results are as follows. 1) Metro-integrated bike-sharing trips are most highly concentrated in commuting hours, primarily near downtown stations, and in areas with easy access to the metro system, while metro-integrated ride-hailing trips demonstrate a more even distribution between morning and evening and are more closely associated with job centers, especially those with inadequate metro service coverage. 2) Compared with bike-sharing, ride-hailing is a more effective shared mobility mode for addressing the “first- and last-mile” issue at night and at stations located far from job centers, but bike-sharing is more complementary to the metro system during peak hours and near downtown stations. 3) The complementary effects of shared mobility are only identified within a certain range. Once the number of shared mobility arrivals exceeds a certain threshold, the effects of bike-sharing become limited and those of ride-hailing shift toward negative. Based on our findings, we provide policy recommendations for better integration of shared mobility services with the metro.

Shared electric scooter services (SESS) have gained popularity in many cities as an emerging mobility mode. However, SESS is attributed to low utilization rates in some cities. In this context, the significance of users’ satisfaction with the provided services, along with their loyalty, becomes particularly pronounced. Therefore, it is essential to evaluate the service quality (SQ) from the users’ perspective and understand its effect on satisfaction and loyalty, which are critical factors for the long-term durability and success of service. Thus, this study examines the determinants of user satisfaction and loyalty toward SESS. For this, we conducted an online survey among the SESS users in Chicago. We analyzed the responses using partial least squares structural equation modeling (PLS-SEM) and reflexive thematic analysis (TA). Our results confirm the quality-value-satisfaction-loyalty paradigm and indicate that users’ perceptions of SQ, including safety and security, service availability, interface, and perceived affordability, significantly contribute to user loyalty enhancement toward SESS. Perceived value (hedonic value, utilitarian value, and perceived affordability) and satisfaction mediate the impact of SQ on loyalty. Additionally, the results indicate that those who perceive SESS as cost-effective report higher satisfaction levels. Furthermore, users who have pro-environmental attitudes and view SESS as environmentally friendly are more likely to derive hedonic value from the service and exhibit higher loyalty. The TA uncovers key challenges from the user perspective, including pricing, service timing, end-trip facilities (e.g., parking), street pavement condition, vendor operational zoning, and fleet quality, with a specific emphasis on GPS accuracy. These findings provide valuable insights into user behavioral mechanisms, which can be considered as a manifest for service improvement and enhancing efficiency.

Urban drivers frequently experience challenges posed by adverse weather conditions like heavy rain. Despite the influence of these conditions on individual travel behaviour, there is limited understanding of how various aspects of rainfall affect the route choices in urban trips. In this context, this paper aims to evaluate the impacts of different rainfall conditions on the route choice behaviour of private vehicle users. We propose a method to identify the specific routes taken by drivers, utilizing observed trips reproduced from a large-scale dataset containing license plate information recognized by speed device cameras. Path Size Multinomial Logit models penalizing routes that share common links were estimated based on a choice set comprised of routes with a maximum similarity threshold. Distance and the actual travel times in the routes were estimated using real-world data from a third-party company, and cumulative, average and maximum rainfall through each route of the choice set were obtained from data captured by meteorological radar. A case study in the city of São Paulo, Brazil, considered the average and maximum rainfall intensities, distance, and travel time in different models. The results indicate that both average and maximum rainfall intensities negatively impact the utility of routes. This research enables the identification of routes with the highest probabilities of being chosen during intense rainfall. Such information is valuable for implementing measures to minimize the impacts caused by adverse weather conditions in urban trips.

A limited number of studies using static spatial approaches have found that built environment variables are associated with bike share use and fewer have used spatially dynamic activity spaces to examine these relationships. The aim of this pilot study was to examine associations between built environment characteristics of daily activity spaces and bike share using three different geographic information system methods. Thirty-two adult members of Boston’s Blue Bikes bike share wore a GPS unit for up to 7 days. GPS points were used to create buffered track, minimum convex hull (MCH), and standard deviational ellipse (SDE) activity spaces. Multilevel logistic regression was used to estimate associations between docking station density, overall bicycle network density, shared-use trail density, intersection density, land use mix, and greenness, with bike share use. Bike share station density within SDE activity spaces showed a significant positive association with bike share (odds ratio (OR) = 1.19; 95 % confidence interval (CI): 1.02, 1.39). Total bike network and shared-use trail densities within MCH activity spaces were positively associated with bike share (OR = 1.13; 95 % CI: 1.02, 1.26 and OR = 1.75; 95 % CI: 1.06, 2.89, respectively). Intersection density within SDE activity spaces was inversely associated with bike share (OR = 0.91; 95 % CI: 0.83, 0.99). GPS tracking of individuals allowed for spatially and temporally dynamic identification of environmental exposures potentially relevant to bike share use. Overall, the findings are consistent with prior research on the environmental correlates of bike share and reinforce the importance of bicycle infrastructure to support greater bike share use. At the same time larger studies are needed to explore optimal geographic methods to define activity spaces in relation to bike share.