Accommodating taste heterogeneity in railway passenger choice models based on internet booking data

Hetrakul, Pratt; Cirillo, Cinzia

doi:10.1016/j.jocm.2013.04.003

Cited by 41 publications

(18 citation statements)

References 11 publications

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“…Utilizing individual customer purchase data to predict quantities of cancellations and no-shows has been confirmed effective in the field of airline research, as illustrated in the work of Lawrence et al (2003), Garrow and Koppelman (2004), Neuling et al (2004), Gorin et al (2006), and Illiescu et al (2008). Similar research has been conducted in the railway system, but as far as the authors are aware, there are only the works of Cirillo et al 2011, Hetrakul and Cirillo (2013), Piening et al (2013), Hetrakul and Cirillo (2014), and Chen and Wang (2013). However, these two fields are still focused on applying their results to decide the amount of seat overbookings and the allocations of seats among different fare classes, rather than identifying an individual's value to decide how to respond to the individual's request.…”

Section: Introductionmentioning

confidence: 77%

Untitled

2016

JPT

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This paper integrates a customer segmentation method with a discrete event simulation model to bridge the gap between identifying customer behaviors and using this knowledge to respond to customers and make the best use of resources. Three strategies are proposed and examined to improve the operation efficiency of a ticket-booking system. Their objective is to assist high-value customers in obtaining the tickets they want and/or reduce cancellations and failure-to-pays from low-value customers. Our simulation results demonstrate that the high-value, customer-friendly strategy beats all in assisting high-value customers and simultaneously improves railway operation performance. Additionally, the indirect, low-value customer abandonment strategy also has improved slightly in all aspects. Applying these strategies is expected to result in a decrease in complaints regarding booking system rejections and an increase in high-value customer satisfaction. On the other hand, the direct abandonment strategy to reject all low-value customers does not make any improvement.

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Section: Introductionmentioning

confidence: 77%

Untitled

2016

JPT

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“…Similar research has been conducted in the railway system, but as far as the authors are aware, there are only the works of Cirillo et al (2011), Hetrakul and Cirillo (2013), Piening et al (2013), Hetrakul and Cirillo (2014), and Chen and Wang (2013). However, these two fields are still focused on applying their results to decide the amount of seat overbookings and the allocations of seats among different fare classes, rather than identifying an individual's value to decide how to respond to the individual's request.…”

Section: Introductionmentioning

confidence: 88%

“…Similar techniques are also applied in the railway industry. Hetrakul and Cirillo (2013) applied three logit-based ticket purchase timing models and compared their suitability to three market segments with different travel distances. Additionally, Piening et al (2013) analyzed customer choices to upgrade, downgrade, or cancel their ticket discount cards when their cards were due.…”

Section: Using Pnr In Ticket-booking Servicesmentioning

confidence: 99%

Can You Get a Ticket? Adaptive Railway Booking Strategies by Customer Value

Wang¹,

Huang²

2016

JPT

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“…As the simulated annealing algorithm has a good adaptability and strong robustness [22,23], it is used to solve the above optimization model. The general structure of the solution algorithm is developed as shown in Algorithm 1.…”

Section: Algorithmmentioning

confidence: 99%

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

Tao

2019

Mathematical Problems in Engineering

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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.

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Accommodating taste heterogeneity in railway passenger choice models based on internet booking data

Cited by 41 publications

References 11 publications

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Can You Get a Ticket? Adaptive Railway Booking Strategies by Customer Value

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

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