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Highlights • A MIP model is built to improve the OD accessibility for the last train services. • A timetable-based Dijkstra algorithm is designed to compute the OD accessibility. • The model and algorithm are tested in the real-life metro network. • The model outperforms the existing transfer model in OD accessibility optimization. Abstract In urban rail networks, passengers usually make transfers from one line to another during their trips. Toward the closure of daily service, certain destinations will become inaccessible for last trains as the connecting service may have already been closed when passengers arrive at the transfer station. This study focuses on timetable synchronization of last trains to improve the network accessibility. A mixed integer programming model is first proposed to determine the scheduled time of last trains. The objective of the model is to maximize the weighted sum of accessible origin-destination (OD) pairs for last train services on the network, which indicates the percentage of passengers using last trains at origins who can reach their destinations successfully. A simple network is used to highlight the difference of the proposed model from existing transfer models in the literature. To solve for large networks, a genetic algorithm combining with a timetable-based Dijkstra's algorithm is developed. A real-life metro network is applied to evaluate the proposed model and solution methodology in practice. The results indicate the proposed model and algorithm enhances the network accessibility, as well as the transfer connections. Comparison analysis shows that the proposed model significantly outperforms the transfer model in network accessibility optimization. [ABSTRACT FROM AUTHOR]

Abstract: By following a computer-aided simulation procedure, this research analyzes the traction energy cost and transport operation time per 10,000 passenger-kilometers of two representative types of metro trains in China under various top speeds between different stations along a hypothetically straight and smooth metro line, from the perspective of both energy saving and transport efficiency improvement in consideration of multi-factors. It is empirically confirmed that if the transport distance between stops is shorter than 1,800 m, the metro trains should set their maximum speeds lower than 70 km/h but higher than 30 km/h. And a shorter stop-spacing requires a lower maximum speed in this speed range to get the least costs of energy and time. The exact value of the maximum speed in this speed range ought to be further determined based on the integrated performances of the train’s passenger capacity, engines, streamline body design, etc. If the transport distance is longer than 1,800 m, the generalized expense of energy and time per 10,000 passenger-kilometers decreases with the increase of the maximum speed of a train. Nevertheless, such decreases become very slow when the maximum speeds of the trains exceed 70 km/h. [Copyright &y& Elsevier]

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences. [ABSTRACT FROM AUTHOR]

Autonomous vehicle technology is poised to revolutionize shared vehicle systems, offering the potential for increased efficiency and convenience. To better devise management strategies for shared autonomous vehicles, this paper addresses a real-time dispatch problem with hybrid requests, where on-demand (immediate) and pre-booked (reserved) trip requests coexist. The coexistence of these two types of request behaviors introduces considerable complexity to real-time dispatch due to the uncertainty in trip demand. We design an approximate dynamic programming (ADP) approach for making vehicle–trip assignments and vehicle relocation decisions. We first formulate the real-time vehicle dispatch problem as a dynamic program and decompose it into time-staged subproblems. To effectively handle the high-dimensional state space, we replace the value functions with tractable approximations and propose a piecewise-linear functional approximation method that captures the spatiotemporal value of vehicles. To calibrate the parameters in the approximations, we propose DualT and DualNext algorithms to provide precise dual information, thereby enhancing the accuracy of our approach. Furthermore, we propose a lookahead strategy that incorporates pre-booked request information into the ADP approach for improving real-time decision-making. We validate the effectiveness of the ADP approach through numerical experiments conducted using taxi data from Brooklyn, New York. The ADP approach outperforms benchmark policies in solution quality while maintaining computational efficiency, and the incorporation of the lookahead strategy significantly enhances the performance of the ADP approach, yielding substantial improvements. Numerical results demonstrate that integrating pre-booked requests into vehicle dispatch management can greatly enhance the system efficiency. • We address a real-time SAV dispatch problem with hybrid on-demand and pre-booked trip requests. • An ADP modeling and algorithmic approach is proposed. • A lookahead strategy is integrated to improve the performance of the ADP approach. • The results demonstrate the potential for pre-booking behaviors to enhance the system efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced. [ABSTRACT FROM AUTHOR]

• An effective opportunistic model arranges multi-level maintenance for multi-component systems. • A reliability model evaluates complex degradation processes concerning imperfect maintenance. • Embedded dynamic strategy coordinates the maintenance schedules on the system and components. • An adapted renewal theory accommodates the maintenance optimization for variable horizons. • Real case studies are carried out to prove the efficiency and generality of proposed models. A suitable maintenance schedule is crucial for large-scale, complex multi-component systems undertaking a long-term operation to reduce failure risks and improve availability and profitability. Opportunistic maintenance is a popular solution when cost consideration is paramount. However, the extensively adopted single-level preventive maintenance action and single-stage degradation process limit the development and application of opportunistic maintenance. Multi-level preventive maintenance including both perfect and imperfect maintenance actions on multi-stage degradation is considered within an opportunistic model to address this challenge. A reliability evaluation model is first formulated to describe a multi-stage degradation process involving the effect of imperfect maintenance. An opportunistic model is then proposed to arrange reliability proportion thresholds for components in systems. The cost rate is considered as the objective function to accommodate the opportunistic model to variable scheduling horizons. A flexible dynamic strategy is developed within the opportunistic model to coordinate the schedules on the system and components by updating component schedules once a system maintenance action is executed. An improved hybrid genetic algorithm combining differential evolution is adopted to optimize the complex problem. The case studies on a locomotive system provide a better understanding of the proposed models and demonstration of its effectiveness, generality, and robustness. [ABSTRACT FROM AUTHOR]

In this work, the particulate matter less than 10 μm (PM 10) emissions from a medium-sized passenger vehicle's front brake wear were studied using a finite element analysis (FEA) and experimental approaches. The world harmonised light-duty vehicle test procedure-brake (WLTP-B) cycle was chosen to simulate real-world driving. An electrical low-pressure impactor (ELPI+) was used to count the brake wear particles on a brake dynamometer sealed in a chamber. In addition, a machine learning method, namely, extreme gradient boosting (XGBoost), was employed to capture the feature importance rankings of braking conditions contributing to brake wear PM 10 emissions. The simulated PM 10 emissions were quite consistent with the measured ones, with an overall relative error of 9%, indicating that the proposed simulation approach is promising to predict brake wear PM 10 during the WLTP-B cycle. The simulated and experimental PM 10 emission factors during the WLTP-B cycle were 6.4 mg km−1 veh−1 and 7.0 mg km−1 veh−1, respectively. Among the 10 trips of the WLTP-B cycle, the measured PM 10 of trip #10 was the largest contributor, accounting for 49% of total PM 10 emissions. On the other hand, the XGBoost results revealed that the top five most important factors governing brake wear PM 10 emissions were dissipation energy, initial braking speed, final rotor temperature, braking power, and deceleration rate. From the perspective of friendly driving behaviour and regulation, limiting severe braking and high-speed braking has the potential to reduce PM 10 emissions from brake wear. [ABSTRACT FROM AUTHOR]

• A collaborative transportation plan of passengers and freight on urban rail lines. • Both the dedicated freight trains and existing passenger trains to transport goods. • A MILP model to jointly optimize train stopping plans and schedules. • An iterative scheduling approach to solve the problem. • A case study based on the UP Express in Toronto and Beijing New Airport Line. This paper develops an operational strategy in which urban rail transit is used for freight transport. An environment-friendly urban freight transportation alternative is analyzed by employing optimization techniques to support the collaborative transportation of passengers and freight. Practical cases are investigated to test the technical feasibility of this transportation scheme. The paper formulates the train service design problem on a single urban rail line with passenger and freight. Passenger trains have a prescribed timetable which is allowed to be slightly modified to facilitate the freight service. Freight can be transported by inserting dedicated freight trains or utilizing the extra space inside the passenger train carriages. Station platforms are able to load and unload both goods and passengers. An optimization model for combined train service design is proposed to maximize profit resulting from the balance of revenues and costs brought by the freight service. The efficient schedules of trains and freight allocation plans are to be determined. This problem is formulated as a mixed-integer linear programming model. An iterative scheduling approach which includes a pre-processing method and two heuristic iterative algorithms is designed to solve the model. Two numerical examples are introduced to demonstrate the efficiency of the proposed model and the iterative scheduling approach. [ABSTRACT FROM AUTHOR]