Travel Behaviour and Society最新文献

Understanding passengers’ behaviour during metro disruption can help operators adopt remedial strategies. Passengers will re-plan the departure time, trip-chaining, and travel modes upon getting disruption information. Previous literature has limited research on this behaviour. Hence, we conducted a two-week survey during the COVID-19 pandemic, collecting 3,400 samples to analyze the joint choice behaviour of passengers in Chongqing, China, regarding departure time, trip-chaining, and travel modes. The 12 Cross-Nested Logit (CNL) models were established to explore the passengers’ joint choice behaviour in 12 different disruption scenarios. Analysis results reveal that on weekdays, passengers tend to choose immediate departure, simple trip-chaining, and bridging buses. On weekends and holidays, passengers prefer immediate departure when the disruption duration is less than 60 min. Whereas when the disruption duration exceeds 60 min, passengers are more inclined to postpone departure time, which suggests that operators need to pay more attention to the potential risk of an increased number of hindered passengers in these scenarios. The CNL structure analysis shows that the nest-specific coefficient for “trip-chaining” is the smallest in the weekday scenarios, indicating a high correlation between simple trip-chaining and complex trip-chaining. The results also imply that when exogenous variables change, passengers primarily adjust their departure times and travel modes. Notably, in the 12 models, the frequency of travel cost and waiting time being significant variables is higher than that of transfer counts, indicating that passengers are more sensitive to travel cost and waiting time. And elasticity analysis also shows that passengers are more sensitive to the waiting time for bridging buses. This study can help planners adopt effective strategies to maintain the reliability and sustainability of transportation systems.

Mixed-traffic crosswalks are a prevalent scene in urban traffic. Understanding the movement characteristics of bidirectional e-bike-pedestrian mixed flow is helpful to enhance traffic efficiency at an intersection. In this research, a controlled experiment on bidirectional e-bike-pedestrian mixed flow was conducted. There are three variables, namely the proportion of e-bike riders (i.e., 0.5 and 0.3), the directional split ratio (namely, 1:1, 1:2 and 1:5) and the presence of obstacles on both sides of the crosswalk (i.e., yes and no). Petrack software were used to extract the participants’ trajectories. Passing time, detour ratio, selection preference, fundamental diagrams, the temporal and spatial characteristics and congestion level are analysed depending on the trajectories. It is discovered that a higher proportion of e-bike riders combined with a smaller directional split ratio contributes to increasing passing time of e-bike riders. With the decreasing directional split ratio, the average detour ratios of both pedestrians and e-bike riders decrease. With the decreasing proportion of e-bike riders, the average detour ratio of pedestrians increases, while that of e-bike riders decreases. The average crossing speed of the minor flow is greater than that of the major flow. With the decrease of the directional split ratio, the congestion level in the measurement area also reduces. Pedestrians in balanced flow are more likely to cause large congestion degree than e-bike riders. These results are beneficial for the design of crosswalks, pedestrian crossing management and the configuration of traffic signals.

A plethora of studies have investigated the nonlinear correlation between the built environment and metro ridership. However, the spatiotemporal heterogeneity of this relationship from the perspective of ridership segmentation has received little attention. To address this gap, this study capitalizes on data collected from Wuhan, China. We employ a sophisticated amalgamation of quantile regression models and machine learning methods to construct direct ridership models (DRMs) for different ridership segments (low, medium, and high) and distinct temporal intervals (weekdays and weekends). The primary objective of these models is to scrutinize the salient factors that influence metro ridership within the context of spatiotemporal heterogeneity, including nonlinear relationships and threshold effects of the built environment. The research findings reveal pronounced differences in the significant influencing factors of the built environment on metro ridership across various ridership segments and temporal periods. Additionally, conspicuous spatiotemporal heterogeneity is discerned in the nonlinear relationships and threshold effects between the two. Consequently, considering the spatiotemporal heterogeneity inherent in metro stations, targeted policy optimization measures fostering the sustainable development of transit-oriented development (TOD) strategies are essential.

Wheelchair accessibility of transportation service hailed using Uber and Lyft is fraught with contention. In this research, I interview 12 drivers on the apps who work in Washington, DC to understand their experience and perception about issues surrounding service to wheelchair users. Some drivers experience transporting wheelchair users as markedly different from service to non-wheelchair users due to the uncompensated labor they perform when assisting wheelchair users and the additional time required. They perceive service decline by drivers to possibly stem from lack of compensation for their time and work. One solution to address the problem could be to use app-technology to keep a record of ride requests by wheelchair users who volunteer to disclose disability status and incentivize drivers for completed rides. The overarching purpose of the study is to create knowledge that can contribute to overcoming potential barriers to full inclusion of disabled riders in the app-hailed transportation.

In psychology, choices are influenced by either goal-directed or habitual systems, which are represented by model-based (MB) and model-free (MF) reinforcement learning algorithms. This paper introduces these concepts into the transportation literature and shows that individuals with habitual tendencies often fail to consider new, potentially superior, alternatives. Across two experiments, participants chose between two bus options, with a third, objectively superior option introduced in a later phase. In both experiments, participants engaging in habitual behavior were less likely to explore the new alternative. Furthermore, we found habitual tendencies were moderated by self-reported stress levels, which is consistent with previous results in the psychological literature. This implies that new transportation modes or routes may remain underused as travelers adhere to habitual patterns, disregarding the introduction of potentially better alternatives, and prompt for public policies that aim to promote goal-directed behavior.

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.