Transportation最新文献

Shared parking allocation effectively enhances the utilization of parking resources by implementing a staggered sharing system. The price attached to allocated parking spaces plays a crucial role in determining the interests of both the supply and demand sides, as well as the platform involved. Existing models ignore the lost benefits to demanders after secondary or multiple allocations. This paper constructs a bargaining model for the transaction price of parking spaces between the platform and suppliers in the shared parking system. First, the advantages and disadvantages of existing pricing methods are analyzed in detail. Next, the bargaining process is described, and the model is constructed by the negotiation loss factor, deterrence benefit transfer and platform concession factor. The Harsanyi transformation is applied to solve the model. Finally, the article analyzes the impact of deterrence benefits and platform concessions on three-party benefits in different situations. And Tianyi Square is used as a case study for simulation to realize the shared parking space allocation and pricing process.

This research develops an optimization model to quantify and minimize the risk associated with the variability of travel costs in freight tour synthesis (FTS) transportation problems. The model integrates objectives of maximizing trip entropy to generate diverse solutions alongside minimizing costs. Demand and supply parameters are modeled using fuzzy logic to incorporate the inherent uncertainty of these data. A recent extension of the Markowitz investment model was adapted to account for cost variability. The primary goal is to equip transportation planners with a decision-making tool that incorporates uncertainty. A comparative analysis of different objectives is conducted to assess the effects of risk on outcomes. The multi-criteria TOPSIS method was utilized as a robust tool to select solutions for this multi-objective problem. Results reveal that solutions with high entropy and variability balance risk and flexibility, while those minimizing variance offer more stable solutions with lower risk. Consistently minimizing total cost resulted in the lowest costs, but at the same time, it led to the highest risk. The membership level showed that maximizing entropy and minimizing variance generated similar compliance levels, with lower variability in the case of entropy. Cost minimization resulted in lower compliance, indicating a trade-off between cost and effectiveness in the transportation network. The research demonstrates that prioritizing risk minimization enhances the average membership level compared to approaches focused on cost minimization or entropy maximization. By effectively managing and reducing uncertainty in transportation networks, the developed model serves as a tool for managing and reducing uncertainty in a transportation network, enhancing resilience. Furthermore, its adaptability offers potential applications across diverse aspects of logistics and industry, providing a framework for designing more robust, adaptive, and effective operational strategies.

Out-of-school time (OST) activities are essential for students’ educational achievement and workforce development. However, economically disadvantaged youth have far less OST access compared to their wealthier counterparts. In sprawling, low-density metropolitan areas, a lack of reliable and cost-effective transportation services constitutes a considerable barrier to OST participation for youth from these families. Research on youth travel focuses on school trips and compares active travel modes, such as biking and walking, to motorized modes. However, there is a gap in understanding the travel choices, preferences, and perceptions of underrepresented youth to access OST activities. This study conducted a revealed preference and stated preference survey of underrepresented youth in metropolitan Kansas City, KS-MO, aged 13–18 years old, to capture their travel behavior and interest in utilizing different modal options and evolving technologies in transportation to access OST activities. Five realistic Stated Choice Experiments pivoting the level of travel time and cost for each alternative (i.e., sharing a ride, hailing a ride, renting bikes/scooters, and taking public transit) were conducted. The study used several machine learning (ML) algorithms to predict students’ mode choice preferences at aggregated and individual levels. Among the ML algorithm approaches deployed here, the boosting-based ensemble learning models outperformed the other ML models. The results showed that most students had a low probability of switching from their current mode of transportation across the different choice scenarios, possibly due to a lack of equitable and sustainable transportation options for students to engage in OST activities and uncertainty in the role of evolving technologies in transportation to address this lacking.

This paper presents an approach to use GPS-based travel behavior surveys to determine who is being served well and who is being served poorly by the transport system. We draw on the extensive literature on travel behavior, which has shown that people’s travel behavior is at least in part shaped by the travel barriers they experience. Starting from this basic insight, we define 17 parameters that may provide insight into a person’s relative ease of movement. These ease of movement parameters cover dimensions related to trip frequency and diversity (e.g., overall and in evening hours), transport mode use (e.g., as driver or passenger), travel speed (e.g., for public transport legs), distance (e.g., trip detour ratio), and effort (e.g., ratio between trip legs and out-of-home activities). No single parameter is sufficient to determine whether someone is served well or poorly by the transport system, as behaviors may be the result of choice as well as constraint. However, we argue that jointly the parameters are likely to differentiate well-served from poorly-served people. We apply our approach to data from six GPS-based travel behavior surveys conducted in Israel’s four main metropolitan areas (N = 62,981). We calculate z-scores for all ease of movement parameters, with negative values suggesting mobility problems and positive values relative ease of movement compared to the entire sample. We conduct four known-group analyses, comparing mean z-scores by level of access to a private motorized vehicle, age, gender, and disability. Results are systematically in line with expectations: population segments identified in the literature as experiencing (more severe levels of) transport disadvantage show systematically lower composite mobility scores. These outcomes are particularly striking, considering the short observation period of only one day per respondent. Taken together, these findings provide a first indication that revealed travel behavior patterns can be used to identify population segments poorly served by the transport system and thus to determine both success and failure of the existing transport system. While more research is needed, the approach also holds promise to determine the impacts of transport projects on people’s ease of movement.

This study aimed to provide a multiangled comparison of the impact of real and virtual setups on the behaviour and perception of electric scooter (e-scooter) riding to examine the external validity of the virtual environments in the micro-mobility context. For this purpose, a within-subject design experiment was conducted to collect data on the behaviour of e-scooter riders. Furthermore, self-reported data on mental and physical demand as well as physiological data in the form of heart rate measurements and electroencephalography (EEG) were recorded to provide an additional insight into the behavioural results. The analysis was based on the multinomial logit model (MNL) for the behavioural data, ordered logit models for self-reported measures. Parametric and non-parametric tests were performed to capture the differences in physiological data. The results of the behavioural data showed significant differences in braking and acceleration patterns between virtual and real scenarios, which undermined the external validity of virtual environments in the current context. Further, self-reported measures painted a mixed picture when looked at jointly with biometric measures, where the questionnaires indicated that both setups were indifferent with respect to mental demand, while the physiological data suggested that virtual scenarios were more mentally engaging for the e-scooter riders.

In this study, the transportation network design problem (NDP) is investigated by integrating geometric and topological indices—continuity, regularity, betweenness centrality, and closeness centrality—alongside the traditional total travel time minimization objective. Each index is individually optimized within a bi-level optimization framework, enabling a comprehensive assessment of its impact on network performance. Unlike conventional travel time-based optimization, these indices exhibit lower sensitivity to factors such as time-of-day demand variations, as they primarily capture the intrinsic structural properties of the network. Applications to the Sioux Falls and Tehran networks under different peak-hour scenarios demonstrate that incorporating these indices enhances network cohesion, improves accessibility, and promotes sustainable mobility. Notably, closeness centrality consistently delivers superior performance while maintaining computational simplicity, making it a promising metric for future transportation planning. Furthermore, a multi-objective optimization approach was applied to the Tehran network, where the weighting scheme was derived from a distance-based analysis. Interestingly, the results reveal that solving the NDP with a single-objective function based on closeness centrality yields outcomes remarkably close to those obtained from the multi-objective formulation. By integrating these alternative indices, this research advocates for a paradigm shift in network design that extends beyond operational efficiency, fostering more resilient, accessible, and user-centric transportation systems.

The COVID-19 pandemic has heightened the need to better understand travellers’ preferences for reduced crowding in public transport. Using panel data from repeated choice experiments with a large sample of Norwegian metro and train users during and after the pandemic, we investigate how the marginal valuation of travel time varies depending on crowding levels and passengers’ positions when sitting or standing. Based on mixed logit models and likelihood ratio tests, we find evidence that position does indeed matter: standing close to the door is preferred over other standing positions in the carriage. Regarding COVID-19, we find that crowding costs are lower after the pandemic but cannot reject the hypothesis that this reduction is independent of position. A key novelty of our study lies in the choice card layout, which contributes to the literature on crowding valuation by providing strong evidence that choice card presentations specifying passenger positions yield higher estimated crowding costs compared to variants where position is not specified.

Quality in public transport is a widely discussed topic from both the user's and operator's perspective. With respect to the passenger’s standpoint, the aim of this research was to ascertain whether (and in what way) the traveler’s “quality perception” of high-standard stations could be differently affected by his/her individual attitudes/perceptions, such as to influence mobility choices. To this end, a mobility survey was performed in Naples (Italy) where two metro options, comparable with respect to service characteristics and the connections delivered, differ only in the quality standard of the stations. A binomial Hybrid Choice Model with Latent Variables (LVs) was estimated, jointly with a traditional Logit model as a benchmark. Three LVs proved significant and able to model/quantify the relevance of individual attitudes/perceptions (of “comfort”, “art” and “safety”). Estimation results show that users with an average comfort perception are willing to spend up to 15 min/trip (2.67 Euro/trip) more for high-quality service; users with an average art perception are willing to spend more time traveling (9 min/trip or 1.5 Euro/trip). Furthermore, for this specific (and perhaps unique) case study investigated, the station with greater attention to aesthetics quality is also perceived as safer than other.

The concurrent availability of shared bikes (DBS) and emerging shared e-bikes (EBS) systems offers new opportunities for sustainable urban mobility, particularly in enhancing first/last-mile connectivity with metro systems. However, a significant gap exists in understanding user choice behavior between DBS and EBS for metro connectivity. As shared micromobility options, DBS and EBS exhibit more competitive and complementary relationships compared to other transport modes when integrated with metro services. This study aims to bridge this gap by exploring the factors that influence the choice of DBS and EBS as metro connection modes. Taking Kunming—a Chinese city where both modes coexist—as a case study, the study identifies DBS and EBS trips connecting to the metro using operational data analyzed through the K-dimensional tree method (KDTree) along with Kernel Density Estimation analysis (KDE) methods. A Light Gradient Boosting Machine (LightGBM) model analyzes nonlinear effects in both to-metro and from-metro scenarios across four aspects: socioeconomic attributes, travel characteristics, the built environment, and transportation facilities. The results indicate that transportation facilities and the built environment significantly influence DBS and EBS user choices for metro connections, with notable nonlinear effects. For instance, cycling distance significantly influences mode choices. Initially, the probability of selecting EBS increases with cycling distance, then stabilizes. The likelihood of choosing EBS initially decreases and then increases as road non-linear coefficients rise in two modes. These insights deepen our understanding of DBS and EBS user choices for metro connections, improving the integration of these modes for first/last-mile journeys.

Evaluating spatial access to urban opportunities (such as jobs) has been an emergent approach to studying urban inequities. Unlike in developed countries, where scholars have identified a significant correlation between socioeconomic inequality and residential segregation, Mexico City exhibits relatively high levels of inequality but not extensive segregation in terms of educational outcomes. What is the role of this phenomenon in job access for workers with different educational attainments? This study in the Greater Mexico City area calculates access by public transport using a competitive measure and evaluates the spatial overlapping with other potential socioeconomic groups at a disadvantage. Transit potential needs indices were computed using scaling and exploratory factor analysis. Then, a spatial overlap analysis evaluated where the most significant gaps exist. Results showed that job access by transit decreases with increasing levels of education. This was explained by the location of stationary jobs in specific central corridors for the highly educated and a relative increase in educational outcomes of the population living toward the outskirts (increasing spatial mismatch). The gaps with each transit potential needs index were discussed regarding geographical patterns and potential population groups at a disadvantage. This work contributes to a better understanding of transit disadvantage conditions in the developing context.