Dynamic discrete choice model for railway ticket cancellation and exchange decisions

• Published in: Transportation research. Part E, Logistics and transportation review Logistics and transportation review, 2018-02, Vol.110, p.137-146
• Main Authors: Cirillo, Cinzia, Bastin, Fabian, Hetrakul, Pratt
• Format: Article
• Language: English
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2017.12.004

•Dynamic discrete choice models (DDCM) are applied to a revenue management problem.•Decisions are modeled over time.•Rail users decide to keep their ticket, cancel or exchange their ticket.•Departure time alternatives are included in the choice set.•Predictions obtained from DDCM are close to the observed choices while the MNL significantly under predicts the keep decision. The increasing use of internet as a major ticket distribution channel has resulted in passengers becoming more strategic to fare policy. This potentially induces passengers to book the ticket well in advance in order to obtain a lower fare ticket, and later adjust their ticket when they are sure about trip scheduling. This is especially true in flexible refund markets where ticket cancellation and exchange behavior has been recognized as having major impacts on revenues. In this paper, we propose an inter-temporal choice model of ticket cancellation and exchange for railway passengers where customers are assumed to be forward looking agents. A dynamic discrete choice model (DDCM) is applied to predict the timing in which ticket exchange or cancellation occurs in response to fare and trip schedule uncertainty. The problem is formulated as an optimal stopping problem, and a two steps look-ahead policy is adopted to approximate the dynamic programming problem. The approach is applied to real ticket reservation data for intercity railway trips. Estimations results indicate that the DDCM provides more intuitive results when compared to multinomial logit (MNL) models. In addition, validation results show that DDCM has better prediction capability than MNL. The approach developed here in the context of exchange and refund policies for railway revenue management can be extended and applied to other industries that operate under flexible refund policies.