Multimodal Transportation最新文献

Urban Air Mobility (UAM) is poised to revolutionize transportation, necessitating an assessment of public acceptance before broad commercial adoption. This study presents the Urban Air Mobility Acceptance Model (UAM-AM), which draws from the Technology Acceptance Model (TAM) and underscores the crucial role of initial trust, technology belief and perceived risk. The UAM-AM is validated using Structural Equation Modeling (SEM) based on 544 questionnaires for the first time in China. The findings highlight the significant impact of perceived ease of use and perceived usefulness on acceptance, uncovering a complex interplay with the intention to utilize UAM services. Notably, initial trust emerges as a foundational factor, influencing attitudes directly or indirectly through perceived ease of use and perceived usefulness. Moreover, the research identifies technology belief and perceived risk as fundamental drivers of initial trust. Examination of demographic segments reveals a heightened technology belief among individuals with backgrounds in science, indicative of a more favorable attitude towards UAM adoption. In closing, the paper presents recommendations for policymakers, service providers, and eVTOL manufacturers to formulate effective strategies that promote public acceptance during the initial phases of UAM deployment.

Ride-hailing system requires efficient management of dynamic demand and supply to ensure optimal service delivery, pricing strategies, and operational efficiency. Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner to forecast demand and supply-demand gap in a ride-hailing system poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting these spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.

Transforming railway stations and their surroundings into multimodal transportation hubs (MMTHs) involves numerous actors at different scales and from different economic sectors and levels of government. Successful transformations offer a wide range of benefits to sustainable development, including increased public transportation use and mixed-use, high-density station districts. Intensive collaboration and, ideally, co-creation are critical to achieving these outcomes through MMTHs; however, orchestrating all involved actors is challenging and requires supporting methods, and the knowledge required to develop and refine methods is scarce and rarely subjected to systematic analysis. Based on 15 semistructured interviews and two design thinking workshops attended by 13 and 20 MMTH experts, our study shows that the challenges of co-creating MMTHs relate not only to professional matters but also to managing collaboration and implementation among a large number of actors with various roles and interests. In this paper, we develop design guidelines for reviewing and evaluating two current methods and a prototypical method (the functional model) with the goal of identifying potential improvements and supporting MMTH co-creation in Switzerland. These guidelines cover the broad spectrum of co-creation activities, from organization and design collaboration with relevant actors to the development of a shared vision to support financing and the planning process. The functional model encompasses many aspects of the design guidelines and closes gaps between actors across different scales, economic sectors, and governmental levels. Due to the relatively low effort involved, the method can be repeated as needed throughout MMTH development, which often takes several years. Our study demonstrates that existing MMTH co-creation methods require improvement, and the design guidelines developed here suit this purpose. Our work thus contributes to the further development of MMTH co-creation methods, ultimately supporting sustainable development such as CO₂ emission reduction and responsible land use.

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

In an effort to capture travelers’ propensity towards micro-mobility options, a consumer survey was designed and conducted in the state of Florida in Fall 2021. In addition to collecting socioeconomic, demographic, attitudinal, and trip-related information, stated-preference scenarios were presented to the respondents, in which they were asked to choose between their current mode, and three different micro-mobility alternatives, namely: e-scooter, e-scooter + public transit, and moped. A machine learning classification model, the tree-based Extreme Gradient Boosting algorithm was applied to study users’ mode choice toward micromobility options given its non-parametric nature and high predictive power. SHAP values were then used to analyze the contributing factors for each of the micro-mobility options. In addition, Local Interpretable Model-agnostic Explanations (LIME) was employed to interpret and validate the SHAP findings at the individual prediction level. Model results show that age, car-oriented attitudes, lack of familiarity/previous experience, and lack of appropriate infrastructures were the major barriers to choose micro-mobility services. Such services can be suitable alternatives for young people who come from large families or ride-share users who have prior experience with micromobility services. Among different micro-mobility alternatives, mopeds were favored by males and green travelers. It seems that e-scooter + public transit was considered a safe and comfortable option, especially for students and low-income individuals, but generally not favored by travel time-sensitive or green travelers. Finally, e-scooters seem to be a favorable option for younger individuals with short travel distances. Our findings provide additional insights on policies that may help encourage the use of micromobility devices and promote sustainable, affordable, and equitable mobility services.

In recent years, new concepts such as synchromodality have emerged to help carriers better leverage existing capacities and assets to achieve environmental and socio-economic sustainability. Synchromodality is a vast concept. It involves the intelligent utilization of various transport modes. Its main objective is to enhance the freedom and flexibility to switch between transport modes at transport network nodes. The emergence of synchromodality can be facilitated by optimization and simulation models associated with a sharing web service for decision-making. This article studies the concept of synchromodality in the scientific literature and highlights approaches using simulation and optimization techniques. The major challenge of this study lies in the effective implementation of synchromodality concept in practice, while respecting the instructions and constraints set by freight transport stakeholders from a more generic point of view. For that, we present an implementation of the modal shift on the Seine Axis Corridor. A simulation-optimization framework is proposed to generate reliable transport solutions based on the user preferences and environmental considerations. Finally, we resort to sensitivity analyses to assess the impact of variation of service times.

Human mobility is mostly dominated by the use of private cars, leading to disproportionate carbon emissions, resource consumption, traffic jams, and pollution. Public transport, with buses, trains, etc., can mitigate these issues via its higher pooling potential. However, often times, public transport is considered less convenient and is therefore avoided. Here, we study a bi-modal public transport system consisting of a rail bound line service and a fleet of on-demand shuttles providing connections to the line service stops, aiming at fast transit at low energy and resource consumption. By means of agent-based simulations and analytical theory, we demonstrate that bi-modal transit indeed has the potential to significantly reduce energy consumption of human mobility at reasonable service quality. We further investigate the influence of the stop density along the rails upon the performance of the bi-modal system. We find that within a range of realistic technical parameters, additional stops tend to impede train speed without significantly enhancing the overall performance of bi-modal transit in terms of service quality and energy consumption. Hence, it can be beneficial to reduce the number of stops within an existing railway system and to implement bi-modal transit as a complement.