Transportation最新文献

Cycling on rural highways presents comfort and safety concerns due to the large speed differentials between bicyclists and automobiles. A shortcoming of the current operational methods for rural highways is the limited consideration of quality of service for non-motorized users. This research aims to determine which variables are most relevant to bicyclists cycling (or not) on rural highways and their operational cycling preferences. An online survey collected 982 responses from individuals who cycle on rural highways in the United States. Eight choice tasks were presented with six factors: pavement quality, automobile traffic level, posted speed limit, roadside design, context class, and grade. These factors were obtained from a preliminary survey targeting highway analysis and design practitioners. The results of a mixed-logit model analysis suggest that the presence of a shoulder and its width are the most critical factors to a cyclist’s perceived quality of service on a rural highway. We found that the difference between rural-town and suburban contexts was minor compared to rural contexts. The results demonstrate how cyclist types influence their overall willingness to cycle and their preferences for scenario-specific attributes. This opens the discussion to include different sets of thresholds to assess service levels as a function of cyclist type, following the level of traffic stress approach, and separate perception models by cyclist type. These results should inform recommendations for future research on improving existing evaluations related to bicycling on rural highways.

This research introduces a multi-module framework to derive weekly representative travel patterns from single-day travel diaries. The methodology first uses hierarchical clustering to group samples with similar activity patterns, followed by progressive multiple sequence alignment to construct day-level representative patterns. These day-level patterns are then merged based on their similarity to create week-level representative activity patterns, ultimately producing archetypal weekly pseudo-diaries. The proposed approach accounts for sequential patterns, activity transitions, and cross-day similarities, providing deeper insights into travel behavior beyond traditional statistical methods. Analyzing weekly activity patterns from this longitudinal data revealed significant insights into travel behavior, including distinct work and non-work patterns across the week. For working groups, shorter work durations on Fridays were observed, and the weekly work duration for teleworkers was found to be lower than that of workplace workers. Although the detailed exploration of weekly activity and time-use patterns provides valuable policy insights, this research primarily focuses on advancing activity-based travel demand modeling by introducing a mathematically robust approach to capturing sequential and temporal activity patterns.

We examine the difference in preferences among different cyclist groups, being the first to examine differences in cycling infrastructure preferences among s-pedelec, e-bike and conventional bike riders. We also examine how the cycling frequency of individuals shapes these preferences. To do so we develop a stated-preference choice experiment varying cycling infrastructure and car traffic features impacting cycling for both main and neighborhood streets. We find that while the sign of the preferences is the same for all cyclist types and is consistent with previous findings from the literature on cycling infrastructure preferences, e-bikers and especially s-pedelec riders do have a lower willingness to pay (WTP) for improvements of cycling infrastructure and are more comfortable in sharing the street space with cars. E-bikers do have similar preferences as conventional cyclists for the most important safety-related elements, i.e. for cycling paths instead of cycling lanes on main streets and “cycling-street” designation of neighborhood streets. For these same features, the WTP decreases with cycling frequency, less frequent cyclists valuing such elements more. At the same time, those who cycle less have a lower WTP for car traffic related features.

The concept of autonomous combined transport utilizes autonomous vehicles for simultaneous passenger and goods transportation and is associated with a reduction in road traffic through bundling effects and economies of scale. Yet, its potential benefits particularly hinge on penetration and utilization rates. To date, few studies have explored user acceptance of systems that integrate passenger and freight flows. In this context, a research gap that is particularly evident pertains to the user acceptance of different concepts within autonomous combined transport. Extending the unified theory of acceptance and use of technology (UTAUT2), this paper examines the effects of six psychological constructs on the behavioral intention to use an autonomous combined transport system. Surveying 1040 respondents from Germany, two distinct combined transport concepts—scheduled and on-demand—were examined to identify key acceptance factors and operational peculiarities. Results from structural equation modelling show that the acceptance of autonomous combined transport systems depends on both, the operational concept as well as the purpose of use, with socio-demographic characteristics featuring different indirect effects per concept. Moreover, we find that performance expectancy, effort expectancy, price value, personal attitude, and trust are significant predictors of behavioral intention across both concepts, while the average order frequency of a potential user has a negative indirect impact on behavioral intention. Results show that an extended UTAUT2 model can conceptualize factors influencing autonomous combined transport acceptance, emphasizing the importance of investigating a user’s behavioral intention based on the specific operational concept.