Transportation最新文献

Waiting Time (WT) stands as a pivotal indicator of the accessibility and equality of ride-hailing service. WT is broken down into two parts: the time taken to match passengers with drivers (matching time), and the time for drivers to pick up passengers (pickup time). Prior research merged the two components, leading to biased results. We aim to individually examine the factors influencing each component, considering demographic attributes of drivers and users, trip characteristics, traffic conditions, and neighborhood built environment (BEs). Using two-week ride-hailing orders collected in Shenzhen, our study reveals that: 1) Trip originating from or arriving at tourist attractions and airports exhibit a shorter matching time but an extended pickup time. 2) Female passengers face bias during the matching process, while female drivers tend to experience prolonged durations in both the matching and pickup phases. 3) Matching time is predominantly determined by trip characteristics, whereas pickup time is more influenced by the neighborhood BEs. 4) The impact of matching time on pickup duration varies across trips, influenced by factors such as passenger gender, idle distance, applied discounts, and additional dispatch fees.

In recent years, there has been an upsurge in intelligent mobility solutions that provide door-to-door services. Although these services offer convenience to certain individuals, it is frequently overlooked that they can lead to welfare losses when accounting for the reduced health benefits that result from reduced physical activity. In this paper, we derive a welfare function of introducing first- and last-mile public transport services. By comparing possible health gains from walking with corresponding accessibility losses, we identify the distance boundaries under which the service fails to be socially beneficial. The results are based on a simulation study and draw on further insights from a recent agent-based model from Copenhagen focusing on first- and last-mile public transport. Although the model is intentionally stylized and may not apply universally to all scenarios featuring diverse population densities, demographic profiles, or transport network layouts, the fundamental conclusion presented in the paper is that first-mile services have minimal welfare impact for average trip distances below 1 km, appears robust even under conservative assumptions. In this case, the probability of failure is almost 100% for any realistic parametrization. This finding implies that planners and researchers should focus on the design of main transit networks and the access and egress of active modes to and from the stations. In particular, door-to-door services covering shorter distances should not be the priority of public funding unless in particular situations or contexts.

In this study, we conducted a comprehensive survey involving a substantial sample size (n = 6,405) of urban daily commuters across four European nations (Germany, the United Kingdom, Poland, and the Czech Republic). Our investigation contributes to an enriched comprehension of the user dynamics associated with Mobility-as-a-Service alternatives and their interrelation with public transit modalities in the context of travel preferences. Specifically, we researched the responsiveness of participants to variations in pricing and travel durations. Additionally, we examine the tendencies of various participant categories, stratified into distinct segments based on shared attributes, toward the adoption of public transportation, MaaS solutions, or private vehicular transport. Our findings highlight the essential role fundamental mobility determinants, such as price and travel time, play in influencing the likelihood of opting for a specific transportation modality. This phenomenon was particularly discernible within the "Unspecified Users" group, which gives us options to alter their behavior. The analytical framework used in our study that combined several mathematical modeling tools provided insight into the choices people make when choosing between different travel options, and our findings may be used by decision makers to create better and more informed approaches to promote sustainable alternatives to the use of cars in urban settings.

Understanding commuter traffic in transportation networks is crucial for sustainable urban planning with commuting generation forecasts operating as a pivotal stage in commuter traffic modeling. Overcoming challenges posed by the intricacy of commuting networks and the uncertainty of commuter behaviors, we propose MetroGCN, a metropolis-informed graph convolutional network designed for commuting forecasts in metropolitan areas. MetroGCN introduces dimensions of metropolitan indicators to comprehensively construct commuting networks with diverse socioeconomic features. This model also innovatively embeds topological commuter portraits in spatial interaction through a multi-graph representation approach capturing the semantic spatial correlations based on individual characteristics. By incorporating graph convolution and temporal convolution with a spatial–temporal attention module, MetroGCN adeptly handles high-dimensional dependencies in large commuting networks. Quantitative experiments on the Shenzhen metropolitan area datasets validate the superior performance of MetroGCN compared to state-of-the-art methods. Notably, the results highlight the significance of commuter age and income in forecasting commuting generations. Statistical significance analysis further underscores the importance of anthropic indicators for commuting production forecasts and environmental indicators for commuting attraction forecasts. This research contributes to technical advancement and valuable insights into the critical factors influencing commuting generation forecasts.

The extensive development of autonomous vehicles (AVs) is set to revolutionise the way of travelling. Research suggests that the introduction of AVs may affect travel behaviour and choices, resulting in long-term changes in land use. Accessibility is an important concept that connects transportation and land use, providing a holistic performance measure for the transport-land use system. However, this concept has not been adequately capitalised in studies that attempt to understand the impact of AVs on location choice decisions. To explore this knowledge gap, we proposed an agent-based simulation framework that integrates with accessibility constraints to study how AVs influence behavioural and location choices. The framework consists of an activity-based travel demand model with accessibility constraints and a dynamic transport assignment model. The accessibility constraints are derived from individuals’ travel time budgets based on activity-travel survey data. We applied the agent-based simulation framework to Clayton, Australia, and focused on discretionary activity location choices. Various values of travel time and vehicle running costs underpinned by the use of AVs were examined. While most studies have concluded that AVs can significantly increase trip lengths for daily activities, our results demonstrate that even when AVs are used, the movement of individuals is still limited by spatio-temporal constraints of accessibility. As a result, we predict that the increase in discretionary trip lengths and their impact on traffic congestion is modest.

Understanding mobility behaviour in cross-border regions is a major challenge for transport policies and the wellbeing of people living near national borders. However, the available relevant information is often limited to a collection of unharmonized national sources. To overcome this limitation, we propose a methodology for building a pooled travel survey, by merging microdata from travel surveys conducted independently in different cross-border areas of the same cross-border region. Anchored in the Luxembourg functional cross-border region as a study area, this paper provides a general, ready-to-use harmonization methodology for pooling travel surveys. It critically discusses the resulting harmonized values of standard daily mobility indicators in regard to the specificities of the study area. Lastly, it shows that the values for the proportions of trip purposes computed with the pooled survey exhibit substantial differences from values obtained using only local travel surveys (up to 6 percentage points, and up to 33 per cent in the total number of trips). This illustrates the added value of a pooled travel survey compared with an unharmonized collection of local surveys.

The freight system’s complexity and significant impact on urban areas necessitate carefully considering sustainable transportation options. The proposed freight transit tour synthesis (FTTS) model, using fuzzy logic and entropy maximization, analyzes freight and transit systems as a multiclass category, exploring scenarios where buses and trucks share infrastructure. The experiments demonstrate that capacity and maximum cost significantly influence the solutions obtained using fuzzy parameters, with ε-values indicating the best solution. Results may vary depending on available data, highlighting the need to explore solutions for different capacity levels if exceeded. The impact of the maximum cost constraint on tour flows is significant, emphasizing the importance of considering cost in optimizing tour flows. The model’s robustness is evident across various subjective value of time (SVT) scenarios. The application of the FTTS model offers a novel approach to estimating tour flows, incorporating traffic counts and fuzzy parameters for immediate, relevant results. The model's multiclass formulation accurately represents real-world traffic conditions, considering congestion in traffic assignments. Overall, the FTTS model holds promise for optimizing tour flows and shared infrastructure between freight and transit systems, aiding decision-makers in urban transportation planning and resource allocation, ultimately leading to improved traffic management and infrastructure usage efficiency.

Self-loop is a unique phenomenon observed in the daily operations of bike-sharing systems, characterized by bike returning to its original starting point after several trips within the bike mobility chain. The bike mobility chain concept involves forming new bike chains with a minimal fleet size. By understanding self-loop behavior, we can optimize fleet management and reduce operational costs. This study specifically investigates the self-loop behavior within the bike mobility chain while considering potential demand, using the case of the dockless bike-sharing system in Shanghai, China. An advanced multiply censored Tobit model is utilized to incorporate potential demand into origin–destination (O–D) data and reconstruct the bike mobility chain. The formation mechanisms of self-loop chains based on the land use and geographic location are analyzed. Our model achieved an R² of 0.871, significantly outperforming the baseline model. The results indicate that 76% of the bike chains can form self-loops within a 2-week period. Campus areas exhibit the highest self-loop rates, while suburban campuses can sustain operations with minimal or no scheduling required. This study not only reveals the back-and-forth behavior but also provides insights for scheduling and deployment strategies to enhance the environmental sustainability of bike-sharing systems.

Residential relocations open a window of opportunity to decrease distances to work and other important daily destinations, such as grocery stores. This study investigates changes in trip distances after residential relocation, using data from a panel survey of 435 movers in Germany. We estimate two structural equation models for changes in commute and shopping trip distance. These models additionally allow us to draw insights into the relationships between spatial structure, travel attitudes, satisfaction with the accessibility of the workplace or shopping facilities, and housing preferences in residential location search. We find that there is a weak indication of an association between residential location choice and changes in trip distances. However, the analysis suggests that especially long trip distances are shortened through relocation. While residents in urban areas travel on average shorter distances, both for working and grocery shopping, only the shopping distance decreases after a move to a more urban location. A preference for urban structures leads to an increase in urbanity after relocation only in the model for grocery shopping trips. Even though long trips before relocation lead to dissatisfaction with the commute, we do not observe a direct effect of dissatisfaction with trips or reasons for moving on trip distances after a move.

The COVID-19 pandemic has significantly impacted the transit market leading to ridership loss and service cuts. Most of the post-pandemic transit market literature has focused on how to attract those who stopped using transit services, however little attention has been given to how rider profiles have changed. To address this gap, we examine 2019 and 2022 data regarding transit commuters from Montréal, Canada. We apply factor and k-means cluster analyses to derive market segments at both points in time considering satisfaction levels, telecommuting rates, and frequency of transit use. We build upon these analyses to report on overall and mode group-level changes in the transit market. Our market segmentation reveals that captive, captive-by-choice, and choice riders still exist in the current public transit market. However, the share of these groups in the market has changed. The proportion of captive and choice riders has increased while captive-by-choice riders have shrunk in size. Moreover, the post-pandemic market has become mostly composed of infrequent riders and higher rates of telecommuting. We further explore these trends by commute mode (i.e., bus only, metro only, and bus and metro users). The findings from this research can be of interest to practitioners and policymakers as they shed light on the evolution of the perceptions and behaviours of segments of transit riders from before to after pandemic.