This study investigates a critical hazard identification method for railway accident prevention. A new accident causation network is proposed to model the interaction between hazards and accidents. To realize consistency between the most likely and shortest causation paths in terms of hazards to accidents, a method for measuring the length between adjacent nodes is proposed, and the most-likely causation path problem is first transformed to the shortest causation path problem. To identify critical hazard factors that should be alleviated for accident prevention, a novel critical hazard identification model is proposed based on a controllability analysis of hazards. Five critical hazard identification methods are proposed to select critical hazard nodes in an accident causality network. A comparison of results shows that the combination of an integer programming-based critical hazard identification method and the proposed weighted direction accident causality network considering length has the best performance in terms of accident prevention.

This paper investigates an optimization problem regarding feeder bus network design for metro stations, based on the wide use of battery-powered electric buses. To characterize the problem, a mixed integer programming model is formulated with the objective of minimizing the total cost of the electric feeder bus system, including the user and operator investment costs. The model considers the constraints of route length, bus stop coverage, vehicle capacity, and depot size. The solution incorporates a multiserver queuing submodel with limited system capacity to explore the relationship among the bus headway, the fleet size, and the number of depot-located chargers. Then a cost-optimized planning methodology is developed to solve these three variables for a given network. To solve the network optimization problem, a genetic algorithm that includes customized operators is developed to guide the solution evolution process. Synthetic case studies are conducted to test the model and the algorithm, and a sensitivity analysis on the important input parameters follows.

This study suggests utilizing digital teaching resources, specifically color U3 nephogram, to enhance the effectiveness of Engineering Mechanics classroom instruction. To establish this approach, the research team created a finite element model based on a challenging problem. By collecting and applying numerous digital materials, we conducted a teaching experiment with 37 students. Results of the evaluation indicate that the experiment improved the quality of instruction and significantly increased students’ interest. The color nephogram proved to be an intuitive and clear tool for helping students grasp abstract concepts and complicated mathematical calculations. Finite element technology allows access to a large number of digital resources and its facilitates innovation in the teaching of Engineering Mechanics.

Skills competition is an important part of vocational education system and it has received more and more attention from scholars. In order to ensure the reliability and stability of the competition work, it is necessary to test the mechanical properties. However, vocational colleges lack of relevant experimental equipment, which has become an important factor restricting the preparation for competitions. In order to solve the problem that the students can not test the mechanical properties of their work, this paper proposes a solution based on simulation technology. According to the test requirements of the students, the finite element model was built and the simulation calculation was carried out on a server. The results show that the reduction is 2.731 mm, which meets the mechanical property requirements of the work. The maximum Mises stress is 61.37 MPa, which is less than the allowable stress. The simulation technology not only solves the students’ testing needs, but also provides data support for the specifications of their work. In addition, the application of simulation technology speeds up the production process of the work and saves the experimental cost. E-education is one of the important trends in the future development of vocational education. Simulation technology can provide rich visual resources and rich data for E-education, which is worth exploring by more researchers.

The driver scheduling problem at Chinese electric multiple-unit train depots becomes more and more difficult in practice and is studied in very little research. This paper focuses on defining, modeling, and solving the depot driver scheduling problem which can determine driver size and driver schedule simultaneously. To solve this problem, we first construct a time-space network based on which we formulate the problem as a minimum-cost multi-commodity network flow problem. We then develop a Lagrangian relaxation heuristic to solve this network flow problem, where the upper bound heuristic is a two-phase method consisting of a greedy heuristic and a local search method. We conduct a computational study to test the effectiveness of our Lagrangian relaxation heuristic. The computational results also report the significance of the ratio of driver size to task size in the depot.