Travel Behaviour and Society最新文献

Concerned by the increasing environmental impact of urban areas and the mobility sector, the study examines mobility in Reykjavík, Iceland. Reykjavík residents have been found to have high emissions in both local and leisure travel. The study aims to explore the connections between urban mobility and leisure travel behaviour using a novel method – activity spaces. The relationship between activity spaces and travel emissions is examined for the first time, based on data of about 700 respondents from a softGIS survey. Connections between activity spaces, local and domestic travel were found. High levels of urban mobility were connected to higher engagement in domestic leisure travel, indicating the presence of a highly mobile lifestyle among Reykjavík urbanites. The reasons for the travel could stem from compensation behaviour, or social network dispersion. In addition, the study points at a lack of functioning 15-minute neighbourhoods and public transit in the capital area. Some differences between income groups were noticed, particularly in urban mobility, pointing at the need to examine the social floor of mobility even within generally affluent societies, so that disadvantaged groups do not get left behind in the sustainable mobility transition. As it has been suggested that people travel for their well-being and to escape the tumult of urban life, the relationship between urban mobility, especially environmental exposure during daily travel, and well-being should be studied. Therefore, the study urges for sustainable mobility transitions and urban planning policies that consider the needs and well-being of citizens in all socio-demographic groups.

The process of urbanization and the rapid development of transit networks transform urban land use properties and individuals’ travel demand, especially in densely populated areas. Traffic surveys reveal that joint activity/travel behaviour constitutes an increasingly significant portion of overall travel behaviours. This paper aims to investigate the spatial–temporal characteristics of individuals’ joint activity/travel patterns (JATPs) within the subway network and explores the factors that influence individuals’ choices among different activity/travel patterns. First, we establish a social relationship classifier to infer acquaintances among individuals. Subsequently, we define four distinct types of JATPs and introduce an algorithm for recognizing these patterns. To implement the proposed framework, we conduct a real-world case study based on subway smart card data from Nanjing, China. Our findings reveal that approximately 19% of daily trips were identified as JATPs, while different JATPs exhibit diverse spatial and temporal characteristics. These differences can be attributed to various influencing factors, including travel/activity time, passenger flow, departure time, and passenger flow/income around tap-in/out stations. In addition, we employ the machine learning classification method XGBoost to predict the discrete choice process of JATP. The classification results reveal the spatial–temporal characteristics of JATP and examine the potential influences of various factors on individuals’ joint activity and travel behaviours. Ultimately, our research sheds light on the evolving landscape of urban travel behaviour in the context of joint activities and travel, with practical implications for transportation planning and policy.

While a substantial body of literature exists on the factors associated with walking behavior, little is known about the long-term effects of childhood experiences. This study utilizes micro-longitudinal data from the Indonesian Family Life Survey (IFLS) to track the lives of 1,343 Indonesians from childhood to adulthood. Findings from a series of regression analyses provide suggestive evidence of the relative importance childhood experiences play in shaping walking behavior during adulthood. Specifically, exposure to a dense urban environment during childhood appears to increase the likelihood of developing sustained walking behavior in later life. Additionally, our findings reveal that individuals raised in households with lower vehicle ownership are more likely to maintain walking behavior during adulthood compared to otherwise similar individuals from households with higher vehicle ownership. In summary, this study highlights the potential long-term efficacy of spatial planning strategies and transportation policies in fostering a more sustainable travel pattern.

Non-mandatory activities (e.g., shopping and leisure) are irregular in space and time, resulting in complex interactions between individuals and urban spaces. Understanding the associated factors of non-mandatory activities is vital for effective urban transport planning and management. This study uses travel survey data from Guangzhou, China, and a random forest (RF) model to investigate non-linear relationships between non-mandatory activities and their associated factors from the perspectives of time, location, built environment, activity dependency, and individual socioeconomic status, on both weekdays and weekends. The contribution of each factor to different non-mandatory activities is examined by a post hoc interpretable method, Shapley Additive exPlanations (SHAP). The results show that activity start time and activity dependency factors have a more significant impact on non-mandatory activities on weekdays, while duration has a greater influence on weekends. Built environment factors like wholesale and retail points of interest (POIs) play a significant role in shopping activities on both weekdays and weekends, while tourism POIs have a greater impact on leisure activities on weekends. Additionally, our analysis reveals the nonlinear dependencies and threshold effects of the top three factors for each category of non-mandatory activities and highlights their disparities between weekdays and weekends.

Travel preferences in public transport (PT) have been substantially affected by the COVID-19 crisis, with rising emphasis on on-board safety and comfort aspects. Hence, real-time crowding information (RTCI) might have become even more instrumental in supporting travel decisions in congested urban PT systems. This study investigates the willingness to wait (WTW) to reduce (or avoid) overcrowding with RTCI in urban PT (bus and tram) journeys, analysing pre- vs. post-COVID travel behaviour attitudes. Stated-preference data and (subsequently estimated) choice models indicate that, while the pre-COVID WTW was primarily driven by mere possibility to avoid an overcrowded first departure, the post-COVID propensity to wait is strongly associated with expectations of seat availability in second departure as well. The ex-post WTW with RTCI seems to have become less-dependent on individual characteristics and more prominent for time-critical (obligatory) trips as well. Our findings underpin the rising relevance of passenger overcrowding in urban PT journeys. Moreover, they help better understand the potential of RTCI in post-pandemic recovery of PT ridership.

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.