Multimodal Transportation最新文献

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.

Park-and-Ride (P&R) facility is crucial in urban transit access and delineation of market area assists in determining its service extent. It is defined with a standardized shape but may be influenced by travel time and mode. This study examines the distinction in the market area for cars and 2-wheelers at different times on weekdays and weekends. Results show a parabolic shape, influenced by travel time, mode, and day of the week in different extents. The market area of the 2-wheelers is smaller than cars, further reduced on weekends. The proposed approach aids planners and operators in accurately defining P&R market areas.

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.

Refueling decisions of liner ships are facing challenges from both fuel price fluctuations and carbon emission constraints. This paper proposes a multistage stochastic programming model to tackle the refueling problem for dual-fuel ships under carbon intensity indicator (CII) rating limit and carbon tax costs. The model also takes into account various factors, including fuel consumption of main and auxiliary engines, fuel availability at ports of call, and fuel price fluctuations. The proposed model is solved using scenario size selection and moment matching methods, and a greedy heuristic algorithm is adopted to speed up the process. Managerial insights are obtained from multinomial logistic regression and sensitivity analyses. Our numerical results reveal that low sulfur fuel oil (LSFO) refueling decisions are closely linked to the difference of LSFO and liquefied natural gas (LNG) fuel prices and that LSFO becomes more attractive when the variance of LSFO fuel price or the LNG availability decreases. Besides, carbon emission costs are found to become a true consideration when carbon taxes exceed a certain threshold. These insights can help practitioners better understand the coupling influence of carbon emissions and fuel price fluctuations on the ship refueling problem.

This literature survey aims to provide a broad view of studies on work from home (WFH) and its effect on travel behaviour published from 1975 to 5 February 2024 by employing bibliometric mapping in VOSviewer. The cluster analysis was deployed to identify collaboration among authors, institutions, and journals, the most co-cited articles and journals, and study terms co-occurrence. The detailed movement of information in each study was visualised using Sankey diagrams. This literature survey was conducted to develop the relationship between WFH and transport-related parameters, including travel behaviour, trip distance, housing location and land use management and to assess WFH's direct and indirect effects on household trips. The results revealed that most studies used questionnaire surveys for data collection and regression models for analysis. The least targeted parameters were home relocation, free choice of WFH, ICT, the effect of WFH on other household member trips per day, cross-country research, trip chaining, employers' perspectives on WFH, exact working location instead of home, and preferences of essential workers. The findings can assist researchers in identifying influential authors or institutions for future collaborations and the combination of parameters and future research directions that can be explored.