Transportation最新文献

Accessibility improvement has been regarded as one of the main regional policy goals in the recent decades. As a result, many countries has undertaken complex transport infrastructure investment programmes worth billions of dollars. The best examples of such investment can be China or, in the European context, Spain and more recently Poland. There have been published many analyses focusing on output and employment effects of transport infrastructure investment projects. Yet, there hardly exist any studies that verify their potential influence on interregional trade flows. This paper fills the existing gap in the literature by assessing the impact of accessibility improvement on the volume and the value of interregional trade flows in Poland between 2005 and 2015. We find that there is no statistically significant relationship between travel time reduction and interregional trade. This may explain largely the surprising lack of positive relationship between accessibility improvement and regional GDP growth, reported in previous papers.

The transition to autonomous vehicles (AVs) will likely vary across countries due to differences in technology advancements, infrastructure, cultural background, and policy. Managing this transition can be challenging, as similar policies may elicit different responses from relevant stakeholders, leading to either societal benefits from the technology or unintended consequences. This study explores the similarities and differences in the impacts of sociodemographic factors, attitudinal factors, and policy-related factors on AV adoption in two countries that are expected to be one of the early adopters: China and the United States. A theoretical framework was developed to examine these impacts, and Multiple Indicators and Multiple Causes models were estimated using 2000 valid survey responses from each country. The model estimation results reveal that certain sociodemographic factors, such as education and income levels, similarly influence AV adoption intentions in both countries. However, other characteristics, like gender and the number of household vehicles, exhibit contrasting effects. Furthermore, attitudinal factors like attitude towards AVs, perceived usefulness, and perceived monetary value significantly impact AV adoption intentions among Chinese respondents, whereas perceived usefulness, perceived nonmonetary value, and subjective norms play a more prominent role in shaping AV adoption intentions among American respondents. In terms of policy impacts, individuals already inclined towards using AVs are more likely to strengthen their intention under all policies, with a more pronounced effect in China, where cultural and economic factors, along with stronger government support for technology, play a significant role.

Quality of life (QoL) in daily travel is increasing in popularity as a research topic since transportation infrastructures and services are instrumental in accessing basic services and social capital benefits in areas such as public health, employment, housing, etc. This accessibility has consequently led to improved QoL for the Bangkok population. In this study, the evaluation of the perception of QoL during the daily travel of Bangkokians in Sukhumvit District, Thailand is conducted using face-to-face interview questionnaires with 500 respondents. The structural equation model (SEM) is employed to quantify QoL and its related multidimensional determinants. Four statistically significant factors affect QoL from the travel perspective: (1) accessibility (p-value 0.001), (2) travel cost (p-value 0.05), (3) environment (p-value 0.05), and (4) information (p-value 0.05). Interestingly, accessibility was found to have the most influence on QoL in daily travel. Therefore, policymakers are recommended to consider the degree to which QoL may be affected to establish transportation policies that are more acceptable, practical, and efficient.

To effectively manage and control public transport operations, understanding the various factors that impact bus arrival delays is crucial. However, limited research has focused on a comprehensive analysis of bus delay factors, often relying on single-step delay prediction models that are unable to account for the heterogeneous impacts of spatiotemporal factors along the bus route. To analyze the heterogeneous impact of bus arrival delay factors, the paper proposes a set of regression equations conditional on the bus location. A seemingly unrelated regression equation (SURE) model is developed to estimate the regression coefficients, accounting for potential correlations between regression residuals caused by shared unobserved factors among equations. The model is validated using bus operations data from Stockholm, Sweden. The results highlight the importance of developing stop-specific bus arrival delay models to understand the heterogeneous impact of explanatory variables. The significant factors impacting bus arrival delays are primarily associated with bus operations, such as delays at consecutive upstream stops, dwell time, scheduled travel time, recurrent congestion, and current traffic conditions. Factors like the calendar and weather have significant but marginal impacts on arrival delays. The study suggests that different bus operating management strategies, such as schedule adjustments, route optimization, and real-time monitoring and control, should be tailored to the characteristics of stop sections since the impacts of these factors vary depending on the stop location.

The growing behaviors of work-from-home (WFH) and online shopping hold significant potential for reducing traffic congestion and emissions. Understanding the frequency and the interplay between these two behaviors is important for successful implementation. This study investigates the recent trends of WFH and online shopping and the underlying factors influencing individuals’ decisions on these two behaviors. Focusing on non-grocery online shopping, this study uses comprehensive survey data collected across the United States during October and November 2021. We develop a Generalized Structural Equation Model to jointly examine WFH and online shopping frequency and their interaction. Moreover, the study investigates the psychological aspects of WFH and online shopping, introducing four stochastic latent constructs—WFH comfort, WFH unproductiveness, online shopping enjoyment, and online shopping inconvenience using the attitudinal variables. Results indicate a positive causal relationship, suggesting that increased WFH promotes online shopping engagement. Perceived comfort and productivity at home affect WFH frequency shaped by factors like home workspace, commuting time, childcare responsibilities, and telecommunications with co-workers. Likewise, perceived convenience and enjoyment significantly affect online shopping, influenced by aspects such as timesaving, and the delivery and return process. Technological tools at home also play a role in WFH frequency. Demographic factors like age, race, income, physical disability, and mode choice habits correlate with WFH and online shopping incidence, while job category and employer flexibility influence WFH frequency. These insights can help policymakers to regulate remote work and online shopping activities as they continue to grow.

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.