Optimization Of Multi-attribute Train Timetables Based On Bi-level Programming Approach For A High-speed Railway Corridor

The construction and the operation of high-speed railway(HSR)network,in large scale,provide the foundation of designing services products of HSR train,being characteristics with oriented demands,reasonable components and multiple services.Passengers' preferences of departing time differ dramatically when they choose HSR trains,that is,the passenger flow presents clear "peak" and "trough" during the different periods of a day.A scientific and reasonable train operation diagram can allocate the transporting resources,meet passengers' various demands and cut down railway industry costs.Besides regulating the exact departing and arriving time at each station,the train operation diagram does also own additional attributes,especially pricing and seat-category,who can largely determine the final distribution of passenger flow on the trains.Conversely,these demands affect making the train operation diagram.Certain high passenger flow periods on HSR shows fully passengers' traveling habits.Due to HSR trains' limited carrying capacity,however,it is quite impossible to meet all these traveling demands of the “peak hours”.Additionally,extra costs of passengers' buy tickets in long queue,waiting in the hall and boarding will emerge because of the high passenger volume and of the corresponding congestion.In viewing of the above profits game between passengers and the railway industry,the bi-level planning theory is used considering comprehensively both passengers' preferences and trains attributes of pricing and seat-category,and optimizing the train operation diagram of HSR trains are studied systematically.The following researches are included in this paper:(1)After reviewing the relevant researches of passengers' traveling demands analysis,train operation diagram optimization and bi-level programming methods,this paper studies the relevant factors of the HSR train operation diagram,such as its characteristics,classification methods,structural elements and optimization principles.According to the passengers' traveling selection behavior,their traveling process is divided into two stages(pre-departing and post-boarding),and the components of generalized costs are designed for each stage in the whole HSR passengers' traveling process.(2)An extended space-time network(ETSN)is constructed for a HSR corridor,and a bi-level programming model is established to optimize the uneven HSR train operation diagram based on passengers' departing time preferences.(3)A time-space-fare three-dimensional network(TSSN)is designed with the strategy of dynamic pricing and a relevant bi-level programming model is proposed,which combines passengers' departing time preferences and the trains' fare attribute with the train operation diagram.(4)With the condition of various seat categories,a time-space-seat three-dimensional network(TSSN)is built.Moreover,a bi-level programming model of TSSN is constructed to optimize an uneven HSR train operation diagram focusing both on passengers' departing time preferences and on the trains' attributes of seat-category.(5)The influences of three factors(including the train departing time,the seat-category and the ticket fare)to the passengers' traveling selection are discussed.A time-space-fare three-dimensional network with the attributes of seat-category(TSFS)is then worked out and a bi-level programming model is built accordingly,which is used to optimize an uneven HSR train operation diagram with taking the departing time preferences,ticket fare and seat-category attributes into account.(6)An upgraded genetic algorithm embedded Frank-Wolfe method is designed to solve all the above bi-level programming models.Also,a compound genetic algorithm embedded Frank-Wolfe method is designed to solve all the above bi-level programming models.With the help of different probability of cross and mutation,both the calculating efficiency and the results' accuracy are enhanced.The multi-attributes HSR train operating diagrams in this paper optimize both system profits and users' ones by cutting down HSR transportation costs and meeting various passengers' traveling demands.All of these make scientific sense to exploit the response mechanism of passengers' choices and HSR train services based on the multi-player game theory.