scholarly journals Joint Decision Model of Group Ticket Booking Limits and Individual Passenger Dynamic Pricing for the High-Speed Railway

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1128
Author(s):  
Zhenying Yan ◽  
Pingting Zhang ◽  
Yujia Zhang ◽  
Hui Liu ◽  
Chenxi Feng ◽  
...  
Keyword(s):  
Dynamic Pricing ◽  
High Speed ◽  
Decision Model ◽  
Joint Decision ◽  
Passenger Demand ◽  
Joint Policy ◽  
Individual Demand ◽  
Expected Revenue ◽  
Group Discounts ◽  
Booking Control

Rail operators in many countries discount group tickets to improve revenue by increasing price-driven demand. For individual passengers, dynamic pricing is beneficial for maximizing revenue based on the price discrimination principle. Usually, group fares are cheaper than individual fares. If too many group tickets are sold, there will not be enough tickets available to meet high-priced individual demand; by contrast, if not enough group tickets are sold and there is insufficient individual demand, the unsold seats will not have value once the train departs. Therefore, for railway operators, it is worth looking for a balance between group discounts and dynamic pricing to maximize benefits. Essentially, rail operators need to find the symmetry point of the expected revenue between accepting group bookings and reserving tickets for individuals when making decisions. In this study, we formulated a joint decision model of group ticket booking control and dynamic pricing and investigated the effect of the joint decision. The results of numerical experiments showed that incorporating group discounts into dynamic pricing can improve expected revenue when passenger demand is weak, and compared to setting fixed quantities for group tickets, dynamically controlling the limit of group bookings can effectively increase expected revenue. Further analysis of the impacts of time, number of tickets sold, and group demand was helpful to implement the proposed joint policy.

The Impact of Structural Reforms and Regulations on the Demand Side in the Railway Industry

2019 ◽  
Vol 18 (1) ◽  
pp. 1-33
Author(s):  
Fumitoshi Mizutani
Keyword(s):  
Market Structure ◽  
Vertical Integration ◽  
High Speed ◽  
Fixed Effect Model ◽  
Structural Reform ◽  
Estimation Methods ◽  
Public Ownership ◽  
Estimation Model ◽  
Passenger Demand ◽  
The Impact

Abstract The main purpose of this study is to evaluate factors affecting passenger rail demand, with special attention to the effects of structural reform/regulation and competition. In order to do this, we use data obtained from 30 OECD countries for the 24 years from 1990 to 2013. As structural reform/regulation and competition variables, we take the OECD’s five kinds of regulatory indices: (i) overall, (ii) entry, (iii) public ownership, (iv) vertical integration, and (v) market structure; and for competition variables, we take (vi) rail passenger-freight ratio, (vii) rail share, and (viii) high-speed train ratio. As estimation methods, both the fixed effect model and the Hausman-Taylor estimation model are used. The major findings are as follows. First, competition as competitiveness (i.e. the share of rail, passenger over freight ratio) increases passenger demand. And the existence of high-speed trains increases passenger demand. Second, overall, entry regulation, and market structure have no significant effect on demand. Third, public ownership affects passenger demand positively. Last, vertical integration reduces passenger demand.

scholarly journals Collaborative Optimization of Dynamic Pricing and Seat Allocation for High-Speed Railways: An Empirical Study From China

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 139409-139419 ◽  
Author(s):  
Xuanke Wu ◽  
Jin Qin ◽  
Wenxuan Qu ◽  
Yijia Zeng ◽  
Xia Yang
Keyword(s):  
Empirical Study ◽  
Dynamic Pricing ◽  
High Speed ◽  
Collaborative Optimization ◽  
Seat Allocation

scholarly journals A Benders Decomposition Algorithm for the Passenger Train Service Planning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Song Pu
Keyword(s):  
High Speed ◽  
Benders Decomposition ◽  
Valid Inequalities ◽  
Service Planning ◽  
Decomposition Algorithm ◽  
Passenger Train ◽  
Railway Line ◽  
Passenger Demand ◽  
Benders Decomposition Algorithm ◽  
Passenger Assignment

Railway transport becomes a more popular transportation in many countries due to its large transport capacity, low energy consumption, and benign environment. The passenger train service planning is the key of the rail operations system to balance the transport service and the passenger demand. In this paper, we propose a mixed binary linear programming formulation for the passenger train service planning to optimize the train route, frequency, stop schedule, and passenger assignment simultaneously. In addition, we analyze the computational complexities of the model and develop a Benders decomposition algorithm with valid inequalities to solve this problem. Finally, our model and algorithm are tested on a real-world instance of the Beijing-Shanghai high-speed railway line. The computational results show that our approach can solve these problems within reasonable solution time and small optimality gaps (less than 2.5%).

scholarly journals Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lianbo Deng ◽  
Jing Xu ◽  
Ningxin Zeng ◽  
Xinlei Hu
Keyword(s):  
Optimization Model ◽  
Dynamic Pricing ◽  
High Speed ◽  
Large Scale ◽  
Solution Method ◽  
Programming Model ◽  
Divide And Conquer ◽  
Small Scale ◽  
High Speed Railway ◽  
Seat Allocation

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.

Tickets Sales Policy for Two-Stage Fares Airline Considering Unidirectional Substitution of Cabin and Swapping Applications

2013 ◽  
Vol 380-384 ◽  
pp. 4541-4546
Author(s):  
Feng Chao Wang ◽  
Zhen Xiu Mo ◽  
Cui Li
Keyword(s):  
Decision Model ◽  
Two Stage ◽  
Expected Revenue

Tickets sales policy of two routes for two-stage fares airline are studied under the following three conditions in this paper: neither unidirectional substitution of cabin nor swapping applications is considered, only unidirectional substitution of cabin is considered, both unidirectional substitution of cabin and swapping applications are considered. We formulate the corresponding decision model and derive the optimal tickets amount and expected revenue. It shows that compared with the condition that neither unidirectional substitution of cabin nor swapping applications is considered, when only unidirectional substitution of cabin is considered, the expected optimal tickets amount and expected revenue increase; compared with the condition that only unidirectional substitution of cabin is considered, when both unidirectional substitution of cabin and swapping applications are considered, the expected optimal tickets amount and expected revenue increase.

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