Transportation最新文献

The COVID-19 pandemic has heightened the need to better understand travellers’ preferences for reduced crowding in public transport. Using panel data from repeated choice experiments with a large sample of Norwegian metro and train users during and after the pandemic, we investigate how the marginal valuation of travel time varies depending on crowding levels and passengers’ positions when sitting or standing. Based on mixed logit models and likelihood ratio tests, we find evidence that position does indeed matter: standing close to the door is preferred over other standing positions in the carriage. Regarding COVID-19, we find that crowding costs are lower after the pandemic but cannot reject the hypothesis that this reduction is independent of position. A key novelty of our study lies in the choice card layout, which contributes to the literature on crowding valuation by providing strong evidence that choice card presentations specifying passenger positions yield higher estimated crowding costs compared to variants where position is not specified.

Quality in public transport is a widely discussed topic from both the user's and operator's perspective. With respect to the passenger’s standpoint, the aim of this research was to ascertain whether (and in what way) the traveler’s “quality perception” of high-standard stations could be differently affected by his/her individual attitudes/perceptions, such as to influence mobility choices. To this end, a mobility survey was performed in Naples (Italy) where two metro options, comparable with respect to service characteristics and the connections delivered, differ only in the quality standard of the stations. A binomial Hybrid Choice Model with Latent Variables (LVs) was estimated, jointly with a traditional Logit model as a benchmark. Three LVs proved significant and able to model/quantify the relevance of individual attitudes/perceptions (of “comfort”, “art” and “safety”). Estimation results show that users with an average comfort perception are willing to spend up to 15 min/trip (2.67 Euro/trip) more for high-quality service; users with an average art perception are willing to spend more time traveling (9 min/trip or 1.5 Euro/trip). Furthermore, for this specific (and perhaps unique) case study investigated, the station with greater attention to aesthetics quality is also perceived as safer than other.

The concurrent availability of shared bikes (DBS) and emerging shared e-bikes (EBS) systems offers new opportunities for sustainable urban mobility, particularly in enhancing first/last-mile connectivity with metro systems. However, a significant gap exists in understanding user choice behavior between DBS and EBS for metro connectivity. As shared micromobility options, DBS and EBS exhibit more competitive and complementary relationships compared to other transport modes when integrated with metro services. This study aims to bridge this gap by exploring the factors that influence the choice of DBS and EBS as metro connection modes. Taking Kunming—a Chinese city where both modes coexist—as a case study, the study identifies DBS and EBS trips connecting to the metro using operational data analyzed through the K-dimensional tree method (KDTree) along with Kernel Density Estimation analysis (KDE) methods. A Light Gradient Boosting Machine (LightGBM) model analyzes nonlinear effects in both to-metro and from-metro scenarios across four aspects: socioeconomic attributes, travel characteristics, the built environment, and transportation facilities. The results indicate that transportation facilities and the built environment significantly influence DBS and EBS user choices for metro connections, with notable nonlinear effects. For instance, cycling distance significantly influences mode choices. Initially, the probability of selecting EBS increases with cycling distance, then stabilizes. The likelihood of choosing EBS initially decreases and then increases as road non-linear coefficients rise in two modes. These insights deepen our understanding of DBS and EBS user choices for metro connections, improving the integration of these modes for first/last-mile journeys.

Evaluating spatial access to urban opportunities (such as jobs) has been an emergent approach to studying urban inequities. Unlike in developed countries, where scholars have identified a significant correlation between socioeconomic inequality and residential segregation, Mexico City exhibits relatively high levels of inequality but not extensive segregation in terms of educational outcomes. What is the role of this phenomenon in job access for workers with different educational attainments? This study in the Greater Mexico City area calculates access by public transport using a competitive measure and evaluates the spatial overlapping with other potential socioeconomic groups at a disadvantage. Transit potential needs indices were computed using scaling and exploratory factor analysis. Then, a spatial overlap analysis evaluated where the most significant gaps exist. Results showed that job access by transit decreases with increasing levels of education. This was explained by the location of stationary jobs in specific central corridors for the highly educated and a relative increase in educational outcomes of the population living toward the outskirts (increasing spatial mismatch). The gaps with each transit potential needs index were discussed regarding geographical patterns and potential population groups at a disadvantage. This work contributes to a better understanding of transit disadvantage conditions in the developing context.

Universities are major trip attractors and generators in large cities, and they have a significant influence on the transport network particularly in high-density areas. The trips to and from university campuses are made by staff, students, and visitors, with an important daily rotation of people (e.g., students who leave early, arrive later, etc.). In this study, we aim to improve our understanding of the trips made to the University of Sydney campuses, one of the largest universities in Australia, through investigation of how individuals (namely, staff and students) choose to study/work from home and their modes of transport used to go to campus on different days of the week. We have collected three sets of data: one in 2022 and two in 2023, using a survey answered by both staff and students. A hybrid logit model including latent variables is estimated to understand the motivations and main drivers to work/study from home and to choose different modes of transport when attending campus. The results indicate that while travel times and costs/fare are important, they are not the primary factors influencing travel behaviour and mode choices. One key factor was whether staff and students worked or studied from home and campus on the same day, with these individuals more likely to use active transport modes, which is also associated with living closer to campus. Students living farther from campus tend to attend more frequently and primarily use public transport. Social connections and a preference for in-person activities are significant motivations that drive different weekly mobility decisions.

In car-dominated cities like Melbourne, Australia, limited data on cyclists’ travel patterns and socio-demographic differences complicate understanding of the effectiveness of infrastructure investment interventions aimed at promoting cycling. Recent advancements in city-scale transport modelling enable virtual testing of such interventions. However, the application of agent- and activity-based models for large-scale cycling simulations has been constrained by data and complexity. In this study, we developed a city-scale agent-based simulation model for Greater Melbourne to evaluate changes in travel mode share from cycling infrastructure modifications. We clustered bicycle riders into five demographic groups: Maverick Males, Motivated Adults, Conscientious Commuters, Young Sprinters, and Relaxed Cruisers, estimating mode choice parameters for each group. Using aggregated smartphone application data, we developed a cycling trip routing methodology to incorporate road infrastructure impacts. Results indicated that travel time significantly influences mode choice across all clusters. Cycling infrastructure was crucial for four clusters, and travel cost influenced four clusters. The calibrated model assessed the potential impact of fully implementing Greater Melbourne’s strategic cycling corridors, a network of key cycling routes. Simulations suggested an initial 30% increase in cycling use, raising the mode share to approximately 2.6%, indicating a modest overall impact. Further analysis showed that even with full implementation, on average about half of the lengths of the routed bikeable trips would still occur on roads without any cycling infrastructure. This underscores the need to improve infrastructure on both major corridors and minor roads, and to complement these improvements with behavioural interventions.

This essay provides an introduction to the field of travel forecasting from a historical perspective. Drawing on the book by Boyce and Williams (Forecasting urban travel: past, present and future. Edward Elgar Publishing, Cheltenham, 2015), I first recount the field’s three developmental phases, from inception to maturation. I then summarize significant innovations in model development, organized under four key themes: equilibrium, integration, behavioral realism and physical realism. This is followed by a general critique of the field that addresses several unresolved challenges, including issues of falsifiability, credulous assumptions, undue complexity, and politics.

Longitudinal studies have become increasingly popular for investigating changes in behaviour, but present additional challenges around participant recruitment, retention, engagement with survey tasks, additional burden and ultimately data quality. Personal technologies, particularly smartphones, have become integral to tackling these challenges but come with their own caveats around user acceptance and engagement. The current paper investigates these issues in the context of a longitudinal study of interventions designed to encourage use of public transport and increase associated physical activity in Tasmania, Australia. The study comprised multiple waves of data collection over a seven-month period in which travel data were collected using a smartphone app supplemented with user experience surveys. Attrition is lower for older participants, those engaging with the app more, and those responding to the research/environmental/health messaging of the survey as well as the potential for financial gain. App usage is lower among older participants while app engagement is stronger for males, those recording less travel and those indicating environmental reasons as a motivator for completing the study. Experiences with the app are mixed, participants report positive sentiments about the ease of use, hedonic motivation, and help in recalling travel; however, concerns are raised over the accuracy of trip recording, the associated burden of correcting trips, and reductions in smartphone battery-life. Despite the unplanned coincidence with the COVID-19 restrictions, outcomes provide important guidance around recruitment, retention and post-hoc analysis of results from longitudinal studies.

In this study, a new approach will be discussed in which routing is done by predicting future traffic and the learning algorithm is optimized during navigation. Traffic has a complex structure that is constantly changing. Especially for long-term travel, it is not an optimum approach to suggest a route only by considering the traffic situation at the time the navigation request is made. For this reason, the proposed algorithm recommends a route by taking into account future saturation conditions on the vehicle’s route. Singapore was chosen as the study area. The tests were carried out in a simulation environment. The four selected algorithms were tested spatially and temporally. Especially in long-term travels, the superior success of the proposed method compared to other selected methods has been demonstrated.

Walking and cycling are widely encouraged to improve safety, promote health and avoid externalities generated by other transport modes, such as air and noise pollution, and greenhouse gas emissions. Many practitioners and policymakers turn to well-established active mobility cultures, such as the Netherlands, to identify best planning practices. However, walking and cycling rates remain low, and arguments are made that besides built environment characteristics, cultural contexts and social norms are also important in encouraging walking and cycling. While travel behaviour is found to be significantly influenced by socialisation factors (e.g. cultural and social norms), the processes of influence are mediated through an intermediate step of past behaviour. In order to understand the role of socialisation factors in changes towards active travel behaviour a whole view of an individual’s life is therefore needed. This study addresses this research gap by investigating the role of long-term socialisation factors and built environment characteristics in the active travel behaviour of high-income immigrants (e.g. expats) living in the Netherlands, using a qualitative, biographical approach. The findings demonstrate that walking and cycling behaviour can significantly change due to the presence of facilitating factors in the built environment, supportive social networks and the normalisation of walking and cycling as modes of transport. People who have grown up and lived in places with little tradition of walking and cycling, can change their travel behaviour if the environment, both physical and social, makes walking and cycling a viable and attractive option to travel instead of using motorised transportation.