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Featured Application: This work can be applied to the rapid and concise evaluation of high-speed railway section capacity. Accurate and convenient assessment of a section's capacity is an essential problem in the operation of high-speed railway companies. The concept of occupancy time is widely used in the field, but there is still a lack of quantitative analysis to support follow-up studies and application. In this research, the calculation method for one train, two trains and multiple trains is discussed. The occupancy time of the two trains was divided into three cases according to the relationship between them, and the calculation formulas are given. For the calculation of multiple trains, the circumstances were too intricate to be classified. To solve this problem, the concept of a continuous train group was proposed. The three properties of the continuous train group were analyzed and proved by mathematical derivation to provide theoretical support to transform a multi-train problem into many two-train problems. Finally, a combined occupancy method for capacity assessment was formed and applied to the Beijing–Shanghai high-speed railway. A series of numerical cases were designed to evaluate the performance compared with the UIC406 method. The results demonstrate that the combined occupancy method can better assess the capacity and is more convenient to implement. [ABSTRACT FROM AUTHOR]

With the steady increase in passenger volume of high-speed railways in China, some high-speed railway sections have faced a difficult situation. To provide more transport services, it is necessary to add as many trains as possible in a section to increase capacity. To solve this problem, a compressed multilayer space-time network model is constructed with the maximum number of trains that can be scheduled in the train timetable as the objective. The combination of the train stop plan and speed level is represented by the layer of network where the train is located, and constraints such as train selection, train safety, train overtake and cross-line trains are considered. A method based on timing-cycle iterative optimization is designed to decompose the original problem into multiple subproblems, and the solving order of the subproblems is determined by a heuristic greedy rule. Taking the Beijing-Jinan section of the Beijing-Shanghai high-speed railway as an example, the maximum number of trains was increased by 12.5% compared with the timetable before optimization. The saturated timetables provide detailed schedules, which helps decision-makers better adjust the timetable to run more trains. [ABSTRACT FROM AUTHOR]

Earthquakes can have a tremendous impact on the operations of university campuses. Since campus buildings are used for teaching, living, and entertaining, it is necessary to establish timely and effective emergency response strategies that accord with the functions of each building. Since there is currently no emergency response system that can provide real-time feedback and enable decision making during an earthquake, this paper proposes a university emergency response system based on multi-source data monitoring. The proposed system offers priorities for rescuing people and maintaining facilities according to five indices: the number of people in the building, the building's command role, the potential danger of hazardous chemicals in the building, the extent of damage to the building, and the lifeline role of facilities in the building. Several methods are used in the process of decision making, including the analytic hierarchy process (AHP), linear regression, earthquake disaster simulation, and expert evaluation. The emergency response system is then applied to a case study of the Shahe Campus of the Central University of Finance and Economics in Beijing, where simulations of three typical scenarios under fixed seismic parameters are carried out and compared. The system proposed in this paper makes the campus's earthquake emergency response more rapid and reasonable, providing new ideas for promoting the development of emergency management systems and sustainable campus operations at colleges and universities. Multi-source data based university emergency response system is proposed. The decision framework is timely and effective under limited cost. Specific buildings' electricity consumption reflects the real-time indoor population. Time characteristics and key areas based emergency resources should be allocated. [ABSTRACT FROM AUTHOR]