In order to better meet diverse travel demands of passengers in designing passenger service products,the multi-dimensional demands of passengers are defined as"further integrating personalized requirements of passengers in terms of time and cost based on OD demands".With the objective of minimizing the generalized travel cost consisting of ticket price cost,travel time cost and departure time deviation cost,a joint optimization model for train line planning and ticket pricing based on multi-dimensional travel demands of passengers is constructed with the premise of maintaining the ticket revenue for high-speed rail enterprises.A solution algorithm based on the Adaptive Large Neighborhood Search(ALNS)algorithm is designed,followed by a case study carried out on the section from Lanzhouxi Railway Station to Xi'anbei Railway Station of the Xuzhou-Lanzhou High-Speed Railway.The results indicate that,after optimization,the travel time cost increases slightly,while the ticket price cost,departure time deviation cost and generalized travel cost decrease by 18.58％,48.10％and 19.17％,respectively.Compared with the Variable Neighborhood Search(VNS)and Simulated Annealing(SA)algorithms,the designed ALNS algorithm shows the slowest convergence speed,but the highest iterative solution quality,improving by 16.62％and 23.87％,respectively.This approach can satisfy the requirement of joint optimization work of line planning and ticket pricing across various route scales in actual production,and provide decision-making reference for optimizing passenger transport products.
关键词
高速铁路/出行需求/列车开行方案/票价/自适应大邻域搜索算法
Key words
High-speed railway/Travel demand/Train line planning/Ticket pricing/Adaptive Large Neighborhood Search(ALNS)algorithm
维普
万方数据