Abstract
Passengers would like to choose the most suitable train based on their travel preferences, expenses, and train timetable in the high-speed railway corridor. Meanwhile, the railway department will constantly adjust the train timetable according to the distribution of passenger flows during a day to achieve the optimal operation cost and energy consumption saving plan. The question is how to meet the differential travel needs of passengers and achieve sustainable goals of service providers. Therefore, it is necessary to design a demand-oriented and environment-friendly high-speed railway timetable. This paper formulates the optimization of train timetable for a given high-speed railway corridor, which is based on the interests of both passengers and transportation department. In particular, a traveling time-space network with virtual departure arc is constructed to analyze generalized travel costs of passengers of each origin-destination (OD), and bilevel programming model is used to optimize the problem. The upper integer programming model regards the minimization of the operating cost, which is simplified to the minimum traveling time of total trains, as the goal. The lower level is a user equilibrium model which arranges each OD passenger flow to different trains. A general advanced metaheuristic algorithm embedded with the Frank–Wolfe method is designed to implement the bilevel programming model. Finally, a real-world numerical experiment is conducted to verify the effectiveness of both the model and the algorithm.
Highlights
High-speed railway (HSR) is a high-quality travel service provided by the railway department to the society to meet the spatial displacement needs of passengers and other additional needs within a specific time range
The train timetable is service information of trains released by service providers, which enables them to make reasonable decisions in their journey. erefore, it is of great practical significance to optimize HSR train timetable
A high-speed railway (HSR) train timetable problem with passenger preferences is introduced; the specific conclusions are as follows: (1) Passenger travel scheme and train timetable optimization are a set of dynamic game relationships
Summary
Wong et al [29] established a mixed-integer programming model to optimize a nonperiodic subway network train timetable problem with the goal of minimizing passenger transfer waiting time. Ibarra-Rojas et al [31] established a biobjective linear integer programming model with the goal of minimizing passengers transferring time and transportation enterprises operating costs to an optimized train timetable. Considering that the interests of service providers and passengers often conflict with each other, for example, reducing the cost of trains may increase the traveling time of passengers, some scholars used bilevel programming method to establish the train timetable model to obtain a system optimal scheme. This paper analyzes the travel behavior of passengers, establishes a generalized cost function for passengers, and uses a bilevel programming to optimize train timetables. For any passengers of OD pair (r, s), from the boarding station r to the alighting station s, passengers will travel through the above various arcs. e sum of the costs paid on all arcs is called generalized cost, known as route impedance, and is shown in gckrs ca · δkrs,a
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