文献类型：期刊文献

英文题名：Multi-Objective Pricing Optimization for a High-Speed Rail Network Under Competition

作者：Cao, Huizhuo[1];Li, Xuemei[1];Vaze, Vikrant[2];Li, Xueyan[3]

第一作者：Cao, Huizhuo

通讯作者：Cao, HZ[1]

机构：[1]Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China;[2]Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA;[3]Beijing Union Univ, Sch Management, Beijing, Peoples R China

第一机构：Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China

通讯机构：[1]corresponding author), Beijing Jiaotong Univ, Sch Econ & Management, Beijing, Peoples R China.

年份：2019

卷号：2673

期号：7

起止页码：215-226

外文期刊名：TRANSPORTATION RESEARCH RECORD

收录：;EI(收录号：20191706833556);Scopus(收录号：2-s2.0-85064634803);WOS:【SSCI(收录号:WOS:000479070500019)，SCI-EXPANDED(收录号:WOS:000479070500019)】；

语种：英文

外文关键词：Costs - Decision making - Economic and social effects - Iterative methods - Linear networks - Profitability - Railroad transportation

摘要：Multi-objective pricing of high-speed rail (HSR) passenger fares becomes a challenge when the HSR operator needs to deal with multiple conflicting objectives. Although many studies have tackled the challenge of calculating the optimal fares over railway networks, none of them focused on characterizing the trade-offs between multiple objectives under multi-modal competition. We formulate the multi-objective HSR fare optimization problem over a linear network by introducing the epsilon-constraint method within a bi-level programming model and develop an iterative algorithm to solve this model. This is the first HSR pricing study to use an epsilon-constraint methodology. We obtain two single-objective solutions and four multi-objective solutions and compare them on a variety of metrics. We also derive the Pareto frontier between the objectives of profit and passenger welfare to enable the operator to choose the best trade-off. Our results based on computational experiments with Beijing-Shanghai regional network provide several new insights. First, we find that small changes in fares can lead to a significant improvement in passenger welfare with no reduction in profitability under multi-objective optimization. Second, multi-objective optimization solutions show considerable improvements over the single-objective optimization solutions. Third, Pareto frontier enables decision-makers to make more informed decisions about choosing the best trade-offs. Overall, the explicit modeling of multiple objectives leads to better pricing solutions, which have the potential to guide pricing decisions for the HSR operators.