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.

The equity-efficiency tradeoff is a perpetual challenge in public transport planning. There is a strong need to integrate equity considerations into transit planning, while respecting the long-term financial sustainability of the public transport system. We introduce an ‘Equity over Time (EoT)’ multi-period, biobjective, bilevel frequency optimization framework, to integrate fairness metrics into bus allocation, while incorporating practical considerations such as fleet rebalancing costs and transfers. By changing the recipient of benefits or penalties over time, the multi-period allocation perspective allows the model to improve the tradeoff between efficiency and equity. We use Pareto-front analysis to demonstrate the improved tradeoffs between efficiency and equity of the EoT approach, then show through numerical experiments that the EoT framework is able to achieve better or equal solutions than its single period counterpart in all instances. We propose a customized matheuristic combining pattern generation and scheduling to solve larger instances. Numerical experiments show that the heuristic is on average 99.03% faster than the exact method for instances that return a solution. The EoT framework is then applied to the southern suburbs in Canberra, Australia. We introduce an Efficiency-Equity Tradeoff Index to support the selection of a suitable planning horizon duration.

Many studies have examined the determinants of travel satisfaction. However, how the perceived ability to travel, i.e., ease of travel (EoT), influences travel satisfaction has not been analysed in a comprehensive way. In this study, we will analyse how EoT, which is comprised of travel motivation, travel skills, travel options and travel quality, impacts satisfaction with travel to campus of 2593 students and staff members of University College London (UCL). One-way ANOVAs show that respondents with high levels of EoT are more satisfied with their trips to campus compared to those with lower EoT levels. Based on linear regressions (per mode and all modes combined), we found that all EoT elements seem to positively affect travel satisfaction, even after controlling for socio-demographics and trip characteristics. This indicates that EoT may be regarded as an important predictor of travel satisfaction. Apart from EoT, also age, mode choice, weather conditions and levels of crowding and congestion were found to significantly impact travel satisfaction. Somewhat surprisingly, effects of travelling alone, trip duration, and travel disabilities on travel satisfaction – which were often found in existing studies – were weak, suggesting that these effects may be partly explained/moderated by variations in EoT elements. In order to make public transport and active travel trips more satisfying, we recommend policy makers to focus on (1) improving the quality of public transport services and active travel infrastructure, and (2) helping people to improve their skills required to easily walk, cycle or use public transport.

A good understanding of activity-travel pattern (ATP) variabilities in transit networks can help government or transit operators improve the accuracy of travel demand forecasts and adjust transport supply. Previous studies on ATP variabilities are often limited to a short period and cannot comprehensively reflect the interpersonal and intrapersonal variabilities. In addition, the traditional clustering methods lack sufficient learning ability for high-dimensional feature space, and clustering variables are simple aggregated indicators. In this study, we propose an ATP inference algorithm that reconstructs multi-week individuals’ discrete trips into an ATP, and the ATP is modeled as stochastic process. Various indicators regarding the standard deviation (SD) of travel time, the SD of number of trips, and the entropy of ATPs are applied, and the entropy rate of ATPs is explicitly considered to take account of the order of activity/travel choices. The intrapersonal variability of ATPs is obtained through these indicators. The interpersonal variability of ATPs is investigated by dividing people into groups based on their ATPs through a deep embedded clustering approach, which refers to a deep neural network formed by integrating the stacked denoising autoencoder with the basic clustering algorithm. The proposed deep embedded clustering is tested using massive smart card data collected from the metro system in Nanjing, China. Comparative experiments validated that the variabilities of multi-week ATPs investigated by the deep embedded clustering approach can help realize a more accurate ATP prediction.

The popularity of e-scooters has introduced both new challenges and opportunities for urban mobility. This paper explores shared e-scooters’ development and regulation internationally through a civic (non-profit-oriented) stakeholder lens. Nine international expert interviews were conducted spanning different types of organisations. Data were analysed using NVivo software. A stakeholder framework was used to identify and examine the needs of stakeholder groups, to aid understanding of behaviours, conflicts, and collaborations within the shared e-scooter ecosystem. The findings suggest that successful partnerships in the e-scooter sector require proactive government leadership, a competitive yet sustainable operator environment, and technological advancements. Clear responsibility distribution in regulation among government bodies and addressing public safety concerns are crucial. This paper also details the interests and interactions among typical stakeholders in a shared e-scooter scheme. By presenting diverse perspectives and experiences from different regions, this paper provides comprehensive insights into the current status and potential future developments of shared e-scooters. The findings offer an evidence base for proposing recommendations to engage stakeholders effectively and foster positive outcomes for shared e-scooter schemes.