Transportation最新文献

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.

Trip-level household travel surveys (HTS) are an efficient and widely used instrument in transport planning and research and are expected to remain in this role for at least the near future. Mode information is typically assigned to trips in these surveys based on a hierarchy of transport modes that hides important information on the individual stages which is particularly relevant for walking. This study develops a methodology for estimating detailed stage-level information for trip-level HTS that contain some information on stages, this is the sequence of used transport modes and the number of transfers in Public Transport (PT) trips. The methodology is developed based on detailed stage-level data from a sub-sample in the German National HTS MiD 2017 and directly applied to the German city-based HTS SrV 2018 which is a trip-level survey but contains stage-level information on modes and PT transfers. Linear Regression Models for estimating walking stage duration in PT and car trips are combined with simple heuristic estimations for the less frequent types of intermodal trips. Trip purpose, accompaniment and total trip duration are important predictors for walking stage duration. Trip-level and stage-level modal-split figures for the number, duration, and distance of trips and stages in SrV 2018 are computed with the developed methodology. About half of all stages and 30% of trips are done by walking. Walking stage duration is with around 38% considerable, this share drops to around 12% for walking stage distance.

This research investigates the interactions among transit (public transport) modes by inferring complementarity from a comparison of access provided by various transit modal combinations over a period of 160 years. A unique historical dataset of transit networks and services including buses, trains, and trams, generated for the Greater Sydney region for a period spanning from 1855 to 2015 was used. Access to population was measured for each year by 11 different modal cases formed by combinations of the three transit modes at a spatially disaggregated level as well as the regional level. The changes in access provided by the different modal cases were compared temporally and spatially to infer complementarity among the modes. Throughout the study period, trains, buses, and trams (when available) were found to be highly sub-additive at the regional level. Spatial comparison of complementarity was also demonstrated for Sydney’s Central Business District as an example. Such an investigation of access by modal combinations is a useful planning tool to ensure equitable supply and to investigate transfer benefits and penalties.

In mobility-on-demand services, the number of vehicles needed is often determined by peak demand during rush hours, leading to prolonged vehicle idle times during off-peak periods. This surplus capacity presents an opportunity for vehicles to perform additional tasks, potentially enhancing system efficiency and reducing the overall number of vehicles needed in cities. Leveraging agent-based modeling, we evaluate the effectiveness of vehicles catering to on-demand rides and food deliveries in two real-life scenarios: Cambridge, MA, USA, and San Sebastian, Gipuzkoa, Spain. The results show that multifunctional behavior can lead to reduced fleet sizes, with context-specific exceptions. Additionally, a strategic dispatching algorithm is introduced that demonstrates reductions in wait times and overall distances traveled. This research contributes to the understanding of the performance of multifunctional fleets in diverse urban contexts, informing the development of sustainable and resource-efficient mobility systems.

Many references to policy paradigms and paradigm shifts can be found in the transport studies literature. Within this literature, diverse ways of interpreting and measuring paradigms and paradigm shifts are evident. This article critically reviews how paradigms are conceived in the transport studies literature and compares these interpretations with the policy science literature where the term has been more widely studied and used. The article proposes a conceptualisation of transport policy paradigms along four key dimensions: problem framing, goals, instruments and evaluation criteria. This conceptualisation helps to increase the clarity and usefulness of the term paradigm for both researchers and practitioners in studying transformative changes in transport policy.

Commuting research often assumes that commuting is a daily grind that elicits stress and strain for travelers. Some scholars have identified evidence that supports the benefits of commuting, but research into this aspect is still relatively limited and does not make a strong connection with the body of works that view commuting as a stressor. This paper offers a more nuanced understanding of commuting by integrating these seemingly separate schools of thought. Drawing from the theoretical lens provided by the Conservation of Resources (COR) theory and research on rest and recovery, we develop and present the Pre-commute/In-commute/Post-commute (PIP) model of commuting, with six propositions. This model views each commute as unique and dynamically shaped by circumstances that occur both before and during the commute. Consequently, the events and experiences that take place after the commute are affected, influencing commuters’ state before their next commute, as well as having an enduring impact on individuals’ health and work-related outcomes. The PIP model also provides a fuller consideration of how individuals can shape the commuting experience through the In-Commute Activities (ICAs) they choose to do. Implications for research and practice are discussed.