Dynamic pricing for railway tickets with demand-shifted passenger groups

Zhu, Ying-ting; Wang, Fuzhang; Lv, Xiaoyan; Pan, Y.

doi:10.1109/icmse.2014.6930238

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…The pricing portfolio of passenger rail carriers is linked to the applications that provide passenger check-ins. Qin et al, 2016 , Zhu et al, 2014 proposed new price portfolio models for different options of ordering rail transport. These pricing models are designed based on economic-mathematical models linked to existing ticketing applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation of travel document purchase measures aimed at preventing the spread of COVID-19 – Case study from Slovakia

Bulková

Čamaj

Šperka

et al. 2023

Transportation Research Interdisciplinary Perspectives

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Section: Literature Reviewmentioning

confidence: 99%

Simulation of travel document purchase measures aimed at preventing the spread of COVID-19 – Case study from Slovakia

Bulková

Čamaj

Šperka

et al. 2023

Transportation Research Interdisciplinary Perspectives

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“…Data-Driven Dynamic Pricing Ranking [11] [12] [22] [3] * [18] ** [23] [ 24] ** [19] [25] ** [26] ** DPRank (ours) Since today's markets are highly competitive, the question often arises as to how a company should evaluate its dynamic pricing system against competitor systems. In this regard, the problem of the quality ranking of existing methods for a dynamic adjustment of prices on public data can be stated.…”

Section: Study Tests On Open Data Open-source Codementioning

confidence: 99%

“…Features of passenger behavior are considered in several studies, e.g., in Refs. [3,18,23], while the distribution of changes in the railway carriage capacity is discussed in Refs. [19,24].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach

et al. 2023

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Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict the impact of dynamic pricing strategies. Publications in the field lack a common quality evaluation methodology, public data, and source code, making them difficult to reproduce. In this paper, a data-driven method, DPRank, for evaluating dynamic pricing systems is proposed. DPRank first builds a surrogate price elasticity of demand model using public data generated by a hidden dynamic pricing model, and then applies the surrogate model to build an exposed dynamic pricing model. The hidden and exposed dynamic pricing models were then systematically compared in terms of quality using a Monte Carlo simulation in terms of a company’s revenue. The effectiveness of the proposed method was tested on the dataset collected from the website of a Russian railway passenger carrier company. Depending on the train type, the quality difference between the hidden and exposed models can vary by several dozen percent on average, indicating the potential for improving the existing (hidden) company’s dynamic pricing model.

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“…And through the use of game theory in a variety of modes of transportation to develop different pricing models. Zhu et al [11] established a dynamic fare model based on the division of passenger groups and the probability of passengers purchasing tickets, and on the basis of optimization theory and decision tree analysis (DTA). Huang et al [12] proposed a new bus fare structure based on non-linear distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

et al. 2020

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This paper proposed improved measures for the shortest path fare scheme of urban rail transit. Firstly, this paper simulated Beijing rail transit by using Anylogic simulation technology and shortest path algorithm. Then, in order to find the travel time between any originations and destinations, this research measured the inbound time, waiting time, interval time, section running time, transfer time and outbound time. In addition, this paper used big data analysis technology to obtain the actual travel time distribution between any originations and destinations by processing the basic data of passengers entering and leaving the station. Finally, by comparing the valid path travel time calculated by any originations and destinations with the actual travel time distribution of passengers, the path taken by majority of passengers was pushed back to determine the ticket price based on the mileage of the path taken by the majority of passengers. The results reduced the dependence on government subsidies by rail transit operation and made up for the operation and maintenance costs. INDEX TERMS Shortest path, Anylogic simulation, travel time, time distribution, big data analysis, pricing scheme of urban rail transit.

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Dynamic pricing for railway tickets with demand-shifted passenger groups

Cited by 6 publications

References 6 publications

Simulation of travel document purchase measures aimed at preventing the spread of COVID-19 – Case study from Slovakia

Simulation of travel document purchase measures aimed at preventing the spread of COVID-19 – Case study from Slovakia

Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach

Big Data Analysis of Beijing Urban Rail Transit Fares Based on Passenger Flow

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