Travel Behaviour and Society最新文献

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.

Metro-bikeshare integration has emerged as a major sustainable mode of transportation for medium and long-distance travelers in various cities. To enhance the satisfaction of integrated metro-bikeshare users and improve the efficiency of urban multimodal transportation systems, this paper proposes integrated path guidance strategies for metro-bikeshare users, tailored to the diverse preferences of individuals. Using the actual smart card data collected from Nanjing, China, a path optimization model is developed to maximize integrated benefits within the metro-bikeshare multimodal network. These benefits include enhancing the overall travel utility of users, reducing the dispatching cost of shared bikes and realizing the load balance of passenger flow. The results show that an 8.89 % increase in total travel utility for all users though the optimization of travel path for 12.51 % of metro-bikeshare users, coupled with an average dispatching frequency of 1.18 times for each transfer node. Furthermore, tailored combined travel path optimization strategies are suggested for “first kilometer”, “last kilometer”, female, male, regular and non-regular users. These findings are helpful for governments and enterprises to formulate personalized path schemes and corresponding path guidance services for metro-bikeshare users.

Bikeshare is increasingly recognized as a healthy travel behaviour worldwide. However, issues of inequity in bike-sharing usage exist and hinder the social benefits of bike-sharing system. This paper aims to unveil the spatiotemporal evolution of inequalities in bike-sharing usage and their social-built environment correlates, using Chicago’s Divvy system as a case study. Specifically, Gini coefficients and panel data regression models are applied to analyse equity concerns in bike-sharing uses and its social-built environmental factors. Thirty-two disadvantaged communities and forty-five non-disadvantaged communities are identified based on ethnicity, income, and education levels. The Gini index indicates a greater level of inequity and inconsistency in bike-sharing usage within disadvantaged communities compared to non-disadvantaged communities over time. Model results further reveal that built environment factors such as park space positively impact equitable bike-sharing uses in disadvantaged communities. In contrast, the social factor of educational levels in non-disadvantaged communities shows a negative relationship. These findings aim to promote essential, efficient, and equitable bike-sharing usage for Chicago, stakeholders and users.

With the advent of new mobility modes and technologies, we have seen meaningful changes in travel behavior. One such new mobility mode is on-demand transit. The Metropolitan Atlanta Rapid Transit Authority deployed its own on-demand transit system, dubbed MARTA Reach, in March of 2022. This paper provides an evaluation of the characteristics of two groups of people related to MARTA Reach: those who were interested in it and used it and those who were interested in it but did not use it. In addition, this paper explores the factors that influence membership in each of those two groups using a binary logit model, revealing the underlying characteristics that are linked with the decision to use or not use the service given prior interest. The findings show that simply providing more service has the strongest effect on adoption. Among 561 survey respondents, 426 expressed that the service area for MARTA Reach was too limited for their needs. Modeling results support this finding, in addition to the following strong predictors of on-demand transit adoption: 1) being a frequent transit user, 2) being satisfied with the current state of fixed-route transit service, 3) being part of a low-income household, 4) living within an on-demand transit service area, and 5) being younger. Understanding these group characteristics and underlying factors can help guide future efforts to provide on-demand transit service, such as by targeting the market segments that share features with the underlying factors that are shown herein to be linked with on-demand transit adoption.