Learning-based dynamic ticket pricing for passenger railway service providers

Kamandanipour, Keyvan; Yakhchali, Siamak Haji; Tavakkoli–Moghaddam, Reza

doi:10.1080/0305215x.2022.2030324

Cited by 12 publications

(9 citation statements)

References 35 publications

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“…Kamandanipour et al [16] proposed a data-driven dynamic pricing method for passenger railway service providers. They utilized a multilayer perceptron artificial neural network as the model and used a regression model as the price elasticity function to quantify the impact of prices, seasonal conditions, and competition on company sales.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Pricing Model for E-Commerce Products Based on DDQN

Sun,

Wang,

Qiao

et al. 2024

JCBAR

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This paper addresses the challenges of intense price competition, price elasticity, and significant demand fluctuations in e-commerce product markets by adopting a dynamic pricing approach. Focusing on a product from the JD.com e-commerce platform, historical data spanning the past three years are analyzed, considering factors such as shipping costs, product inventory, product costs, and the impact of holidays. The study employs the Double Deep Q Network (DDQN) for dynamic pricing optimization of the product and compares its performance with the DQN model. The results indicate that both the DQN algorithm and DDQN algorithm lead to varying degrees of profit improvement for dynamic pricing of products. Specifically, the pricing profit with the DQN algorithm increased by an average of 1.925% compared to the original pricing profit, while the pricing profit with the DDQN algorithm increased by an average of 11.975% compared to the original pricing profit. These findings demonstrate practical significance.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Pricing Model for E-Commerce Products Based on DDQN

Sun,

Wang,

Qiao

et al. 2024

JCBAR

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“…Com base em Kamandanipour et al (2022) pode-se fazer uma adaptação do modelo proposto pelos autores e formular um problema geral para precificação dinâmica de bilhetes do transporte rodoviário de passageiros, cujo objetivo é a maximização da receita. O modelo de otimização deve ser resolvido separadamente para cada dia de partida 𝑇 da viagem de um ônibus.…”

Section: Modelo Geral De Precificação Dinâmica No Transporte Rodoviár...unclassified

Método Exato Para Um Problema De Precificação Dinâmica No Transporte Rodoviário De Passageiros

VERI,

KLEINA

2023

Anais Do Encontro Nacional De Engenharia De Produção

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XLIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUÇÃO"A contribuição da engenharia de produção para desenvolvimento sustentável das organizações: Cadeias Circulares, sustentabilidade e tecnologias" Fortaleza, Ceará, Brasil, 17 a 20 de outubro de 2023. XLIII ENCONTRO NACIONAL DE ENGENHARIA DE PRODUÇÃO"A contribuição da engenharia de produção para desenvolvimento sustentável das organizações: Cadeias Circulares, sustentabilidade e tecnologias" Fortaleza, Ceará, Brasil, 17 a 20 de outubro de 2023.

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“…Ge et al (2021) implemented a combination of differentially ARIMA and SVM to achieve a highly predictive model for passenger flow in Shanghai-Guangzhou railway station. Kamandanipour et al (2022) presented a multi-layer ANN system to forecast the strength of demand caused by seasonal conditions using train ticket service data. Müller-Hannemann et al (2022) investigated a new technique of approximating scenario-based resilience employing XGBoost, Catboost, SVR and ANN models which are based on carefully selected important aspects of public transport systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are two stages to any learning process: (i) Particular a given dataset, calculation of unknown relationships in a system Particular a given dataset, calculation of unknown relationships in a system (ii) predicted connections are used to forecast new platform outputs. Machine Learning has also been shown to be an interesting topic of study in passenger demand prediction, with several applications , Zheng et al, 2021, Hayadi et al, 2021Gummadi and Edara (2018) Kamandanipour et al (2022);Müller-Hannemann et al (2022). The ability to predict passenger traffic in transportation networks is critical to public transportation management.…”

Section: Introductionmentioning

confidence: 99%

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

Utku

Kaya

2022

Decis. Mak. Appl. Manag. Eng.

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Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.

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Learning-based dynamic ticket pricing for passenger railway service providers

Cited by 12 publications

References 35 publications

Dynamic Pricing Model for E-Commerce Products Based on DDQN

Dynamic Pricing Model for E-Commerce Products Based on DDQN

Método Exato Para Um Problema De Precificação Dinâmica No Transporte Rodoviário De Passageiros

Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system

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