Transportation最新文献

Decarbonizing aviation is challenging as few scalable technological alternatives exist, and travel activity is increasing rapidly. It is thus essential to better understand the drivers of air travel behaviour. Previous cross-sectional research has identified a range of factors associated with individual air travel frequency. There is, however, a lack of longitudinal studies identifying the factors associated with change in air travel frequency on the individual level. This is in contrast with research on daily travel and car use, where ‘mobility biographies’ studies have identified the life-course factors associated with travel behaviour change. Our study contributes to filling this gap. We investigate the determinants of change in air travel frequency using data from two waves of the UK Household Longitudinal Survey (2012–2013 and 2018–2019), combined with geographical information at the neighbourhood level. With regression models, we assess the impact of changes in a wide range of factors including socio-demographic and economic situation; residential location; spatial dispersion of social networks; migration status; car ownership; and environmental attitudes. We find significant effects for several variables, including e.g., a negative effect of having children on air travel frequency, and a reduction in the number of flights in the first few years after migrating to the UK. We conclude by discussing how the findings can inform debates on: i) the impact of life-course events on travel behaviour; ii) the causal drivers of air travel frequency; iii) the drivers of air travel growth, and related implications in terms of inequality and ‘institutionalisation’ of air travel.

This study uses the Dynamic Spatial Difference-in-Differences model (Dynamic SDID) to analyze the impact of the Taiwan High-Speed Rail (THSR) on Taiwan’s tourism demand. To control for spillover effects, the model incorporates the Taiwan Tourist Shuttle service (TSHU) as an alternative transportation option, the interactive effects between TSHU and THSR, and the spatial autocorrelation between TSHU and THSR. The analysis results indicate that controlling for spillover effects is crucial for analyzing the impact of the High-Speed Rail and tourist transit service on Tourism Demand, and the Dynamic SDID is a better analytical model for this purpose. The THSR has a significant positive impact on tourism demand, while its spatial autocorrelation effect is significantly negative. This suggests that the increase in tourist traffic brought about by THSR mainly comes from existing tourists in the surrounding areas rather than generating new tourism demand. The TSHU, on the other hand, has a negative but insignificant impact on tourism demand, but its interaction with THSR has a significant positive effect, indicating that the two services complement each other. Therefore, to enhance Taiwan’s tourism demand, the focus should still be on improving the attractiveness of tourist destinations rather than solely relying on the construction of the High-Speed Rail. Additionally, while the TSHU does not contribute significantly to the development of specific individual tourist destinations, it does facilitate regional tourism development. Therefore, selecting TSHU routes based on actual market conditions can promote the growth of the tourism industry.

This paper investigates the impact of conformity on traveler’s route choice and evaluates the value of conformity in this context. Drawing from classic theoretical model of conformity, we analyze the factors influencing conformity in route choice. Based on this analysis, we develop an integrated choice and latent variable (ICLV) model, incorporating latent variables related to conformity, individual characteristics, as well as route-specific attributes. The model parameters are calibrated using data collected from a stated preference (SP) survey in Nanjing, China. Estimation results reveal strong correlations between conformity and travelers’ route choices. The findings of this study carry significant insights for leveraging conformity in the design of navigation software and congestion pricing strategies.

Utilising the existing infrastructure in railway transit to tackle overcrowding requires more understanding of how people use spaces at stations. This study investigated passenger behaviour while waiting for a train on the platform using the data of the Wi-Fi location tracking systems. The trajectories of 129,354 devices were observed in two weeks at two MRT Circle Line stations in Singapore, which have the escalator/stair landings in different positions. A data cleaning process was proposed to overcome the drawbacks of Wi-Fi-based position data. A decomposition method was further developed to separate the walking and staying phases based on data processing. The boarding passengers’ on-platform behaviour was analysed from four aspects: the number of staying phases, the location distributions of different kinds of stays, the location distribution of in-between stays by hour and duration, and the distance and walking speed of the first walking phase. Our results suggested that many passengers (44% and 37% of passengers at the two case study stations) had multiple staying phases, meaning that they did not go directly to their final boarding points after coming to the platform but rather made stops or walkarounds before coming to boarding points. The distributions of locations of the last and in-between stays were significantly different and may influenced by the width, length and layout (such as landing locations) of stations. In addition, the walking speeds of passengers observed on the metro platform were slower than those observed on the streets. These findings indicated that some commonly used assumptions in most simulation models are not true according to the empirical observation. The obtained knowledge would deepen the understanding of the passengers’ on-platform behaviour and thus provide implications for designing railway stations and planning station operations.

This paper presents a novel adaptive stated choice experiment to capture range anxiety during long-distance travel. It is assumed that respondents have forward-looking properties allowing them to select from a set of charging alternatives along the route or postpone charging for a later (choice) stage. Data was collected among members of the Danish electric car association. Based on this data, we develop a mixed logit model that reveals several interesting findings. First, we quantify a relationship between the probability to charge and the remaining range. Secondly, we find that range anxiety, and thereby battery utilisation between recharges is indeed a heterogeneous quantity that varies among user groups. Tesla drivers and individuals below 50 years of age are less prone to range anxiety compared to other segments. Finally, the results suggest that charging at the early stages of a trip is indeed likely even when the battery level is high.

While previous studies have provided insights into the relationship between weather and ridership, how historical and future weather conditions affect bus travel behavior remains to be addressed. And the differences among advancing, current, and lagging effects, between different traveler profiles are not clear. This research aims to fill the gaps by exploring the effects of historical, current, and future weather on bus ridership at hourly scales in Dingjiazhuang, Nanjing, with a typical humid subtropical climate. More than 4 million smart card records, 4 million Global Positioning System (GPS) records, and weather measurements were used over a three-month period. Seasonal autoregressive integrated moving average (SARIMAX) time-series techniques were applied to assess the advancing, current, and lagging effects that five weather conditions, including air temperature, heat index, relative humidity, horizontal visibility, and precipitation, exert on bus ridership at two spatial scales: overall level and origin-destination (OD) pairs. The results showed significant advancing, current, and lagging negative effects of relative humidity on both weekdays and weekends. While current precipitation was negatively associated with bus ridership, the lagging effect was positive. Only significant advancing and current effects of horizontal visibility were captured. Hourly elderly travelers were more affected than younger travelers. In particular, we found that the elderly were more affected by future weather conditions, especially on weekdays. Results yield implications for policymakers to incorporate weather variation information in transit demand monition, which can support requirements for future transport models and develop decision support tools.

This study delves into the potential of dynamic ride-sharing (DRS) systems utilizing the agent-based simulation framework MATSim. Through a comprehensive sensitivity analysis across various scenarios, we investigate the efficacy of a newly developed dynamic ride-sharing extension and unveil key insights. Our findings underscore the pivotal role of user willingness in driving DRS utilization, emphasizing the necessity of flexible departure times to accommodate diverse user preferences. Furthermore, we advocate for the inclusion of short trips within DRS options and highlight the efficacy of incentivizing DRS drivers, albeit with caution regarding unintended consequences such as modal shifts. Despite observing an increase in Vehicle Kilometers Traveled after DRS implementation, our study elucidates the nuanced nature of this increase, particularly regarding unmatched DRS drivers. In a “maximum scenario”, we identify the utmost potential for DRS adoption, shedding light on its viability under conducive circumstances and offering valuable insights for future transportation planning and policy-making.

Our research challenges the prevailing notion that immobility only occurs in exceptional circumstances. Our work shows instead a close link with individuals’ activity levels and constraints on their schedules. We find that retirees exhibit higher immobility levels than workers, influenced by factors such as poor health, old age, low income, lack of access to a car, or rural residency. Analyzing data from last French National Transport Survey, we use structural equation models to examine the impact of various factors on immobility. Driving and walking difficulties are significant contributors to immobility, with age being a primary explanatory factor. However, living in dense urban areas tends to reduce immobility levels across household categories. Difficulties with public transport, as such, do not trigger immobility, but they are entangled with walking difficulties. Implications for public action include targeting vulnerable populations, considering age-specific interventions for reducing car dependency, and approaching policies aimed at curbing older adults’ car use cautiously. Implementing universal design measures to enhance physical accessibility also helps to make mobility smoother and decrease perceived walking difficulties. Finally, this paper underlines the entanglement of mobility and social isolation, emphasizing the need for qualitative and quantitative research in this area.

Transport demand represents the essential basis in the design of public transport services. Information on this is accordingly a necessary prerequisite for the preparation of supply. In the following, a general approach is presented using the example of the European night train market, with which potentials can be derived despite little knowledge of the mobility needs of the market and demand matrices can be drawn up as a basis for economic calculations. By calculating a heuristic rule, the optimal path through a network in line planning is not exclusively a function of the edge evaluations but considers the node potentials determined this way. Transport demand represents the essential basis in the design of public transport services. Information on this is accordingly a necessary prerequisite for the preparation of supply. In the following, a general approach is presented with which potentials can be derived despite little knowledge of the mobility needs of the market. In the context of graph theory, the potential of nodes is evaluated and these are included downstream to the edge selection in form of a rucksack problem. By calculating a heuristic rule, the optimal path through a network in line planning is therefore not exclusively a function of the edge evaluations but considers the node potentials as well. Based on this demand matrices can be drawn up as a basis for economic calculations. Regarding the current debate on the expansion of night train services in Europe, this approach can be adapted to the conception of an overnight train. An overview of all night trains in Northern, Central and Western Europe in 2021 is provided for this purpose and the suitability of the approach is demonstrated using an example. It will be successful in setting up a route network from which passenger flows can be derived. However, possible adaptions that can be considered as second-best solutions are not taken into account and require further development of the approach.

The role of attitudes in location decisions has garnered increased attention in land use-transportation interaction research. However, most research concentrates on one-location decisions at the individual level, leaving the situation regarding multiple-location decisions at the household level largely unexplored. Using household-level survey data gathered in Ganyu, China, this research examines the extent to which women and men in dual-earner households with school-age children reside and work in their preferred built environment in terms of transportation, and how this situation holds different implications for their travel behavior. Descriptive analysis showed that approximately half of the respondents in our sample do not live or work in their preferred built environment. Notably, more men than women do not live in their preferred built environment, although they tend to work in their preferred built environment. Furthermore, the distribution of travel mode shares among consonants and dissonants within different locations reveals that the choice of an active travel mode or an e-bike for commuting, both by women and men, is primarily influenced by the work location, followed by travel-related attitudes and residential location. Finally, a heterogeneous impact of the workplace built environment on travel behavior, based on travel preferences, was identified. However, such implications are only evident for women. Our findings underscore the importance of taking into account gender differences in the role of attitudes in making multiple-location decisions and the subsequent effects on travel behavior in land use-transportation policymaking.