Travel Behaviour and Society最新文献

Hand foot mouth disease (HFMD) is one of the widespread transmissible diseases that target preschool children, especially in urban regions in East Asia. Based on the mobile app data, a GIS automated regionalization method is used to define regions of various urbanicity levels. The variability of HFMD patients’ travel behaviors across these regions are examined by the complementary cumulative distribution functions (CCDFs). The travel burden in Nanchang increased as the patients’ residences moved toward regions of more rurality. Specifically, more urbanized regions near the city center (i.e., urban core and its surrounding urban) enjoyed better access to the healthcare service by spending an average of about 17-minute drive time. Patients residing in less urbanized regions such as suburbs and downtowns of surrounding rural counties either chose a nearby hospital or journeyed far to the region’s central Children’s Hospital, with average elevated travel time of 25 and 22 min, respectively. Those in rural areas travelled an average of 36 min, the longest among all regions. The study also finds a unique dual-peak pattern across two travel ranges in the study area due to the patients’ dilemma between pursuing high-quality hospitals and accommodating travel burden. Such a pattern mandates the adoption of a segmented regression approach to deriving the best-fitting CCDFs in most regions except for the rural.

In order to investigate the role that Mobility as a Service (MaaS) could play in university communities to reduce car dependency and moderate car-oriented travel behavior, this paper examines individuals’ stated interest in adopting MaaS bundles in academic environments, where its potential is still largely underexplored. The study involves a large-scale survey campaign carried out within a university community in Milan (Italy), comprising 1873 answers from faculty members, technical-administrative staff, and students. The paper discusses the factors affecting behavioral intentions towards a potential MaaS adoption on the basis of aggregate statistics and discrete choice models estimates. This research highlights that there is a real opportunity to market MaaS in university communities, but an accurate user-centered design of the MaaS solutions is needed, based on individuals’ preferences and actual mobility needs. Results suggest that MaaS has a broader potential user base among individuals under 35 years old and Public Transport subscribers, and that MaaS bundles involving shared mobility services are attractive by residents in the city center, while reserved parking at interchange facilities is more attractive to commuters coming from suburban areas.

A poor diet has crucial impacts on individuals and society as a whole. In addition to individual preferences, there is evidence that people’s diet is related to their social, economic, and geographical environment, including their mobility context. However, few studies have an empirical, integrated perspective of these previous dimensions. This paper presents an empirical effort to understand people’s food purchasing and eating preferences concerning their mobility dynamics, social networks, habits, perceptions, activities, and sociodemographics, going beyond a static distance-based food supply perspective. The case study corresponds to the city of Concepción, Chile, using data that included nutrition, mobility, social networks, habits, perceptions, activities, and sociodemographics. The relationships between people’s eating and shopping patterns and these different contextual dimensions were analyzed through joint multivariate statistical modelling.

The empirical findings suggest that greater spatial proximity to the purchase of food does not significantly impact the quality of the diet. However, greater proximity to food supply affects the frequency and characteristics of the transport mode to the shopping trips, impacting the diet quality, especially in the case of open street markets. This relationship shows that mobility patterns play an essential role in the impact of spatial accessibility on the purchase of food and the quality of people’s diets. The results also confirm that food consumption and purchasing decisions go beyond individual sociodemographics and are influenced by other aspects of people’s overall mobility patterns, social capital, and attitudes.

In the context of ‘carbon peak’ and ‘carbon neutrality’, coordinating individual travel demand through multi-modal transportation and guiding travelers towards new shared public transportation (PT) modes is increasingly important. In this paper, we analyze the competitive and cooperative relationship between online car-hailing (OCH) services and metro systems in Nanning, China, and conduct a questionnaire survey among different types of OCH users. A mixed choice model that considers psychological latent variables is constructed to investigate OCH users’ attitudes and cognitions toward customized buses (CBs). An improved adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is proposed to identify potential carpooling station sets, and a hybrid genetic-ant colony algorithm (GACA) is designed to solve bi-level programming model for CB line optimization. Case study results indicate an 83.8% overall transfer rate from OCH users to CBs, with the optimized scheme achieving a 69.68% reduction in carbon emissions.

Increasing urbanization is causing many challenges for mobility today, such as traffic jams and high carbon dioxide emissions. Hyperloop is a radical mobility innovation that could offer a potential solution for these issues. Since hyperloop is currently under development, overcoming technical and economic challenges and increasing its acceptance in society will decide the success of this innovative mode of transport. Currently, research on hyperloop user acceptance is limited. This study aims to identify users’ willingness to use the system and factors that determine support or rejection for hyperloop. Therefore, an acceptance model was proposed and then tested in an empirical study based on a sample consisting of N = 387 participants in the Netherlands. The results indicate that performance expectations (e.g., high speed, comfort, environmental advantages) support the acceptance of hyperloop. At the same time, safety concerns (e.g., technology failure, low-pressure environment) were identified as a rejection factor. Based on the results, interested stakeholders should consider the benefits, possible fears, and concerns regarding hyperloop in their communication. Future research should include experience opportunities with hyperloop to obtain more valid results.

In many cities, bike sharing systems, including station-based bike sharing (SBBS) and dockless bike sharing (DBS), are gaining popularity rapidly. Bike rebalancing is one of the most expensive aspects of bike sharing operations, and it takes several hours. In terms of reducing the inefficiencies of frequent short-term bike rebalancing, whether bike distribution achieves long-term self-balance for one day or even longer periods is a critical issue that has received insufficient attention. This paper aims to provide insights into long-term facility planning by investigating the self-balancing phenomenon of shared bikes. It is evaluated using daily stability analyses from the DBS case in Nanjing, China, and the SBBS case in New York, USA. DBS virtual stations were identified throughout the city, and (virtual) stations can be classified into four categories using various clustering methods. The findings demonstrate that 72% of DBS virtual stations and 81% of SBBS stations can achieve bike self-balancing, with only a few (virtual) stations failing to do so. In terms of non-self-balancing stations, bike-increasing stations are primarily located in the city center, whereas bike-fluctuating stations are primarily found near metro lines. This research can assist bike sharing companies with their daily operations and contribute to government management.

The impact of ride-hailing (RH) as an emerging mode of travel service on public transit (PT) systems has been confirmed. However, the current research only views the relationship between PT and RH as competition or complementation based on macro statistics and travel time differences. In fact, the relationship is beyond binary, and it is partial to take the travel time difference as the only classification factor. We constructed a Gaussian mixture model (GMM) using RH data in Xi’an, and three indicators of travel time, cost, and service quality difference were used to classify the relationship between RH and PT. To clarify the factors influencing the relationship classifications, a Multinomial logistic model (MNL) was constructed with the built environment, economic factors, and travel purpose. The results show that the RH-PT relationship can be generally classified into four classifications: Competition (26.5%), RH superiority (47.7%), PT superiority (13.6%), and Irrelevance (12.2%). Competition occurs mainly around metro stations, RH superiority mainly during working hours in outer urban areas, and PT superiority is most widely distributed in the morning peak. POI density and the number of bus lines are positively correlated with Competition, RH superiority, and PT superiority. In addition, there is significant spatial heterogeneity in the RH-PT relationship, for which we constructed a Geographically weighted regression (GWR) model to analyze it. We find that the spatial heterogeneity may stem from the spatial autocorrelation and the spatial disparities in the distribution of regression coefficients. Therefore, policymakers should formulate policies to transform competition from multiple perspectives.

Car-sharing mobility is an emerging sustainable transportation mode, but it poses great challenges to operators and urban traffic management due to the imbalance between supply and demand across time and space. To address the problem, this research proposes a spatio-temporal rebalancing optimization framework for the urban car-sharing system (CSS) based on geospatial big data and machine learning. In the spatial dimension, we construct the urban car-sharing network data set using geospatial big data. The graph deep learning is used to mine the car-sharing space demand patterns for location planning. This data-driven graph neural network approach breaks through the limitations of complex mathematical models in the previous location planning and can cope with large-scale CSS in real time when data is available. In the temporal dimension, we construct a combined optimization model of dynamic relocation and pricing based on the optimized car-sharing station layout. A multi-threaded reinforcement learning algorithm is proposed to solve the optimal relocation and pricing scheme. Dynamic relocation and pricing strategies are obtained by reinforcement learning algorithms based on accumulated historical operational data and real-time market demand, aiming at maximizing profits and optimizing resource utilization. The simulation results show that the combined optimization model of dynamic relocation and pricing provides a more effective solution than the non-combined model. The proposed optimization framework provides systematic decision support for solving urban CSS supply–demand imbalance and yields extensive theoretical and practical implications, especially in urban traffic management.

A rise in reporting and media coverage of negative social interactions and experiences of racism in transit and other public environments suggests that perceived discrimination may have affected the travel behavior and health of people of color during the COVID-19 pandemic. In this study, to examine relationships between race, perceived discrimination, transit use, and walking behavior, we draw on data collected in 20 waves of the Understanding America Study (UAS) COVID-19 tracking survey, fielded July 2020–July 2021. Importantly, we find that transit use among minorities continued during the pandemic at higher rates, especially among Black and Hispanic respondents, despite non-White respondents reporting more frequent perceptions of discrimination. Our linear mixed-effect model results further indicate that non-White respondents were notably more likely to use transit. Examining walking behavior, we find that White and Asian respondents consistently reported more walking than Black and Hispanic respondents, even when controlling for income. Crucially, we found that in the presence of controls, while large disparities were observed in both walking and transit behavior based on race, perceived discrimination had little to no effect. While disparities in travel behavior based on race are evidently better explained by structural factors as opposed to overt, individual-level discrimination, planners, policymakers, and designers should nevertheless give greater consideration to micro- and macro-scale interventions that facilitate safe transit use and walking for racial and ethnic minorities.

Air-rail intermodal services (ARISs) represent a highly promising multimodal solution within the transportation sector. Nonetheless, various uncertainties and challenges persist across multiple dimensions of air-rail interline travel, with discrepancies in passenger perceptions being a notable aspect. In an effort to pinpoint the pivotal factors contributing to these disparities among distinct passenger profiles, this study employs the Structural Equation Modeling-Multiple Indicator Multiple Cause-Artificial Neural Network (SEM-MIMIC-ANN) methodology. This approach explores the impact of numerous attributes on passenger perceptions in the context of air-rail intermodal travel, leveraging questionnaire data gathered from Shijiazhuang multimodal passengers. Furthermore, the study utilizes the Classification and Regression Tree (CART) decision tree algorithm to categorize actual passengers into distinct characteristic groups. Subsequently, the perception levels of these diverse passenger groups are quantified through the calculation of comprehensive evaluation function values. In conclusion, taking into account the real-world conditions of air-rail interline travel, this research formulates a tailored service strategy aimed at enhancing the overall passenger experience.