Transportation最新文献

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.

This article presents the EBIS (e-Biking in Switzerland) study, including its methodology, the resulting dataset, and an initial descriptive analysis of the population surveyed. The EBIS study is a Switzerland-wide GPS-tracking study with the aim of investigating the scope of carbon savings in the transport sector due to e-biking. Participants consisted of cyclists and e-bikers across the French- and German-speaking regions of Switzerland. In addition to extensive GPS tracking, the study included a randomized controlled trial (RCT) on transport pricing’s role in substituting e-bike trips for car trips, a retrospective survey on mode shift, and a stated preference survey on cycling infrastructure preferences. We here report participant attrition and engagement rates and compare the EBIS population to the 2021 Swiss Mobility and Transport Microcensus (MTMC) in terms of socio-demographic characteristics and travel behavior. In doing so, we present a unique dataset of 3,940 participants, 324 thousand user-days of tracking data, and over half a million stages by bike or e-bike.

Emerging modes (e.g., cargo bikes), mobility solutions (e.g., shared mobility services) and policy measures (e.g., the reduction of public parking spaces) are envisioned to reduce private car-ownership. However, the development of disaggregate car-ownership models dealing with them, which can be integrated with transport simulation systems, is still missing. Therefore, this study aims to fill this gap through the development of household car-ownership models, focusing on the cities of Madrid, Regensburg and Leuven. These cities correspond to distinct urban contexts in Europe. Models based on a representative individual as well as based only on household variables are explored, to check whether the latter shows congruence with the former and can achieve good summary statistics with lower data requirements. Furthermore, such models with only household-level variables are also found to be useful in ascertaining the impact of small-scale shared services. The estimation results show the influence of socio-demographic characteristics, mobility patterns, mobility tool ownership, transport supply and urban characteristics. Discussions are included on behavioural and policy insights. For example, cargo bike ownership and the presence of shared services support in car-ownership reduction. Furthermore, public parking spaces have a significant impact on single car-ownership in Madrid, but not on multiple car-ownership. Besides, the contrasting effects found for the mobility rates in Madrid and Regensburg convey the importance of urban design contexts. The contributions from this study enable to estimate the household car-ownership with the consideration of emerging mobility scenarios, and to devise policies to reduce private car-ownership and promote sustainable urban mobility.

The study investigates the latent heterogeneity in travel behaviour among urban travellers, including ride-hailing service (RHS) users and non-users, by incorporating attitudes so as to reinforce conventional user-segmentation approaches. Simultaneously, prioritisation of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in travellers through a Latent Class Cluster Analysis (LCCA) model. Subsequently, it prioritises key RHS-specific attributes for each cluster using three established Multi Criteria Decision Making (MCDM) techniques. Three clusters were identified based on individuals’ attitudes and covariates (socio-demographics, travel habits, and built environment attributes). The largest cluster is the Tech-savvy ride-hailing-ready individuals (48%) with higher technological literacy, showing maximum acceptance towards ride-hailing. The second largest cluster comprises the Traditional active-mobility individuals (28%) who display the least proclivity towards RHS, probably due to their technological inhibition coupled with greater attachment to traditional travel alternatives. Lastly, the PV-loving multimodal individuals (24%) are primarily vehicle owners but prefer RHS for occasional trips. The final ranking obtained from the analysis has revealed that travel time, reliability, and flexibility are the motivators, while travel cost and waiting time are the deterrents, as perceived by the users, that influence RHS in the Indian context.

This paper examines factors influencing the adoption of parking apps and explores various app configurations to reduce reliance on traditional parking meters. The study surveyed 304 respondents in Brisbane, Australia. A mixed logit model was estimated to analyze preferences regarding various app characteristics. Results revealed that the introduction of new contactless payment options, real-time parking information, and parking expiry reminders positively influence the uptake and usage of apps. Conversely, service charges were negatively associated with the adoption of mobile payments. Willingness to pay was also evaluated for the most important attributes that influenced uptake. To further explore the impact of different app configurations, this study simulated three distinct payment scenarios, each offering customers the choice between paying through meters or apps. The simulations showed the need to consider customer backgrounds when offering new payment options. For example, the simulations underscore the significance of offering simpler versions of apps to cater to customers with limited technological proficiency or those seeking a more seamless experience. Maintaining a balanced approach between app-based payments and meters is also recommended, as some customers still prefer traditional payment methods. These insights contribute to enhancing the adoption of apps and guide the implementation of smart parking solutions in cities beyond Brisbane.

In the context of a modal shift towards alternative transport modes such as cycling, walking and public transport, cargo bikes for personal transport could fill a void in the transport market by providing an alternative to car trips, especially among families. Yet, few studies have considered their modal shift effects on the use of other transport modes. We conduct a nationwide survey of 696 cargo bike owners in Switzerland, one of the largest samples to date. Cargo bikes’ modal shift effects are considered through three dimensions (1) their owners’ characteristics, motivations and uses; (2) the substitution of trips by other transport modes; and (3) the renunciation of ownership of other transport modes – especially the car. Our results provide a typology of five types of households depending on how the cargo bike induces a modal shift from the car.