scholarly journals Optimal Differential Pricing for Intercity High-Speed Railway Services with Time-Dependent Demand and Passenger Choice Behaviors under Capacity Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Huanyin Su ◽  
Shuting Peng ◽  
Lianbo Deng ◽  
Weixiang Xu ◽  
Qiongfang Zeng
Keyword(s):  
Optimization Model ◽  
High Speed ◽  
Simulated Annealing Algorithm ◽  
Solution Space ◽  
Capacity Constraints ◽  
Time Dependent ◽  
High Speed Railway ◽  
Distribution Features ◽  
Differential Pricing ◽  
Dependent Demand

Differential pricing of trains with different departure times caters to the taste heterogeneity of the time-dependent (departure time) demand and then improves the ticket revenue of railway enterprises. This paper studies optimal differential pricing for intercity high-speed railway services. The distribution features of the passenger demand regarding departure times are analyzed, and the time-dependent demand is formulated; a passenger assignment method considering departure periods and capacity constraints is constructed to evaluate the prices by simulating the ticket-booking process. Based on these, an optimization model is constructed with the aim of maximizing the ticket revenue and the decision variables for pricing train legs. A modified direct search simulated annealing algorithm is designed to solve the optimization model, and three random generation methods of new solutions are developed to search the solution space efficiently. Experimental analysis containing dozens of trains is performed on Wuhan-Shenzhen high-speed railway in China, and price solutions with different elastic demand coefficients ( ϕ ) are compared. The following results are found: (i) the optimization algorithm converges stably and efficiently and (ii) differentiation is shown in the price solutions, and the optimized ticket revenue is influenced greatly by ϕ , increasing by 7%–21%.

scholarly journals A Line Planning Approach for High-Speed Rail Networks with Time-Dependent Demand and Capacity Constraints

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Huanyin Su ◽  
Wencong Tao ◽  
Xinlei Hu
Keyword(s):  
High Speed ◽  
Simulated Annealing Algorithm ◽  
Capacity Constraints ◽  
Time Dependent ◽  
High Speed Rail ◽  
Line Planning ◽  
Planning Approach ◽  
Upper Level ◽  
Varying Demand ◽  
Dependent Demand

In high-speed rail networks, trains are operated with high speeds and high frequencies, which can satisfy passenger demand with different expected departure times. Given time-dependent demand, this paper proposes a line planning approach with capacity constraints for high-speed rail networks. In this paper, a bilevel optimization model is formulated and the constraints include track section capacity per unit time, train seat capacity, and the gap between the number of starting trains and that of ending trains at a station. In the upper level, the objective is to minimize train operational cost and passenger travel cost, and the decision variables include the line of each train, carriage composition of each train, train stop patterns, train start times, and train arrival and departure times at stops in the line plan. In the lower level, a schedule-based passenger assignment method, which assigns time-varying demand on trains with seat capacity constraints by simulating the ticket-booking process, is used to evaluate the line plan obtained in the upper level. A simulated annealing algorithm is developed to solve the model in which some strategies are designed to search for neighborhood solutions, including reducing train carriages, deleting trains, adding trains, increasing train carriages, and adjusting train start times. Finally, an application to the Chinese high-speed rail network is presented. The numerical results show that (i) the average time deviations between the expected departure times and the actual boarding times of passengers are within 30 min, (ii) the unserved passengers are less than 200, and (iii) the average load factors of trains are about 70%. Hence, line plan solutions meet time-dependent demand well and satisfy the capacity constraints for high-speed rail networks.

scholarly journals Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China

2019 ◽  
Vol 11 (14) ◽  
pp. 3804 ◽  
Author(s):  
Jin Qin ◽  
Wenxuan Qu ◽  
Xuanke Wu ◽  
Yijia Zeng
Keyword(s):  
Sustainable Development ◽  
Prospect Theory ◽  
High Speed ◽  
Simulated Annealing Algorithm ◽  
Peak Period ◽  
High Speed Railway ◽  
Sales Revenue ◽  
Ticket Sales ◽  
Passenger Flow ◽  
Differential Pricing

Based on the single pricing method of the high-speed railway (HSR) in China, a pricing strategy without flexibility leads to the problem of extreme fluctuations in passenger flow and difficulty in increasing revenue. In order to achieve sustainable development of the HSR from the perspective of pricing, in this study, we divided the passenger market according to the different factors affecting passengers’ choice behavior, maximized ticket sales revenue with expected travel cost as the reference point, and used prospect theory to construct a differentiated pricing model under elastic demand. A simulated annealing algorithm was used to solve this model under two passenger flow intensities. Taking the Beijing–Shanghai corridor as an example for analysis, the results show that differential pricing can be implemented on the basis of passenger decision-making, and price reductions at off-peak periods will attract passenger flow which will increase ticket sales revenue by 10.41%. During the peak period, prices can be increased to maintain passenger flow, and ticket sales revenue will increase by 7.98%. We also found that increasing passenger expectations have a greater impact on ticket sales. This study provides theoretical and methodological support for the sustainable development of the HSR.

scholarly journals A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Li Wang ◽  
Yong Qin ◽  
Jie Xu ◽  
Limin Jia
Keyword(s):  
Optimization Model ◽  
High Speed ◽  
Fuzzy Optimization ◽  
Soft Constraints ◽  
Accurate Approximation ◽  
Total Delay ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Tolerance Approach ◽  
Rail Line

A fuzzy optimization model based on improved symmetric tolerance approach is introduced, which allows for rescheduling high-speed railway timetable under unexpected interferences. The model nests different parameters of the soft constraints with uncertainty margin to describe their importance to the optimization purpose and treats the objective in the same manner. Thus a new optimal instrument is expected to achieve a new timetable subject to little slack of constraints. The section between Nanjing and Shanghai, which is the busiest, of Beijing-Shanghai high-speed rail line in China is used as the simulated measurement. The fuzzy optimization model provides an accurate approximation on train running time and headway time, and hence the results suggest that the number of seriously impacted trains and total delay time can be reduced significantly subject to little cost and risk.

A two-layer optimization model for high-speed railway line planning

2011 ◽  
Vol 12 (12) ◽  
pp. 902-912 ◽  
Author(s):  
Li Wang ◽  
Li-min Jia ◽  
Yong Qin ◽  
Jie Xu ◽  
Wen-ting Mo
Keyword(s):  
Optimization Model ◽  
High Speed ◽  
Line Planning ◽  
High Speed Railway ◽  
Railway Line

scholarly journals Dynamic Analysis of a High-Speed Railway Train With the Defective Axle Bearing

2020 ◽  
Vol 25 (4) ◽  
pp. 525-531
Author(s):  
Jing Liu ◽  
Shangkun Du
Keyword(s):  
Dynamic Analysis ◽  
High Speed ◽  
Time Dependent ◽  
Force Model ◽  
High Speed Railway ◽  
The Road ◽  
Reasonable Approach ◽  
Simulation Results ◽  
Train System ◽  
Track Irregularities

Axle bearings (AXBs) are critical parts for high-speed railway trains (HSTs). Local faults in the AXBs have great influences on the operational dynamics of HSTs. Although some previous works formulated the local faults in single AXB, the vibrations of the whole train system with the defective AXB cannot be described. To overcome this problem, this study conducts a dynamic model for a HST considering a local fault in one AXB. The previous single AXB model cannot formulate the studied case. The impacts caused by the fault in the AXB is defined as a time-dependent force model considering a half-sine type. The road spectrum excitations from the roadbed and rail are formulated by a track irregularities model. The effects of the train speeds and fault sizes on the HST dynamics are introduced. The simulation results from the proposed and previous works are contrasted to show the model validation. The results show that the faults in the AXB will greatly affect the HST dynamics. It depicts that this study can afford a more reasonable approach for understanding the dynamics of HSTs considering the defective AXBs compared to the reported single AXB model.

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.

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