Multimodal Transportation最新文献

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.

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.

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.

Recognising the variations in driving behaviour between taxis in the empty and carry conditions is pivotal for enhancing the accuracy of route travel time estimations using floating car data. However, existing methods largely overlook this distinction. In light of this, this study aims to harness these variations for more precise estimations. Utilising taxi data, we segmented the information by service conditions and executed distinct estimations for each segment. The route travel time was deduced through convolutional operation, complemented by a Markov chain model to discern correlations between travel times across various links. Our innovative approach realised a substantial enhancement in accuracy. Notably, when accounting for distinct service conditions, there was a reduction of 51.44% in mean absolute error and a 46.83% decline in maximum percentage error. By providing more accurate and reliable travel time predictions, our methodology enables better-informed traffic management decisions. Accurate travel time estimations are essential for optimising traffic signal timings, planning efficient routing strategies, and managing road network usage. These improvements in traffic management can lead to smoother traffic flow, reduced travel times, and ultimately, diminished congestion in urban areas.

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 %.

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.

Urban multimodal public transport system with rail transit as the backbone and bus as the collaborative operation is an important carrier for the travel of urban residents. For improving efficiency of urban public transport services, it is important to optimize the feeder bus operation plans and promote efficient bridging. A quantitative method based on the data envelope analysis (DEA) algorithm was proposed to effectively evaluate the transfer efficiency of transit hubs and identify service bottlenecks, which used multi-source data including smart card transaction data, bus vehicle operation data and public transport trip chain data based on multimodal individual travel transaction data. Considering the passenger transfer and supply capacity of the feeder bus, and balancing between transfer and regular travel needs, a collaborative optimization model based on the branch and bound (BB) algorithm which can quickly obtain the absolute optimal solution of small-scale optimization problems is established to combinatorial optimize less efficient feeder bus line groups. The Tiantongyuan North transit hub and its feeder bus line in Beijing were selected as the case analysis. The results indicated that the main reasons for the failure of public transport to achieve DEA effectiveness are the departure frequency and the number of operating buses. With the collaborative optimization of the bus routing schedule, the average total transfer time for the transfer passengers is reduced by 19.9%, while the transfer time and waiting time are reduced by 22.8%, 16.1% respectively, which will help improve the efficiency and service level of multimodal public transport collaborative operation.

Bicycle sharing has grown rapidly since the 2000s, but there is a lack of thorough retrospective analysis performed from a long-term perspective. This paper takes London as its focus, aiming to evaluate the performance of the London cycle hire (LCH) scheme, draw general practical implications for bicycle sharing, and summarize future research directions. This paper reviews the empirical evidence that has appeared in academic literature, policy documents, and technical reports. Issues covered in this review include: (1) LCH users and demand patterns, (2) substitutability and complementarity with other travel modes, (3) public health impacts, (4) interventions that have affected the usage and demand patterns of LCH, and (5) the impacts of COVID-19. Overall, LCH has achieved its primary goals of promoting cycling and has also brought benefits to public health and urban transportation resilience, and yet some minor problems persist. Practical implications for the implementation, operation, and evaluation of bicycle sharing schemes are offered based on our collection of evidence.