Passenger Demand For Railway Revenue Management. Final Report

摘要

In this paper, we have illustrated a fare pricing strategy for the Acela Express service operated by Amtrak. The RM method proposed is based on passengers preference and products attributes. Using sales data, a MNL model has been calibrated; the random utility theory has been applied to explain passengers choice of booking time under a range of hypothetical sale horizons. In order to capture aggregate passengers response to fare price, a demand function based on OLS regression has been incorporated in the procedure. This approach is appealing because it allows product attributes such as departure day of week, fare price and destination specific effects to be taken into account in the RM problem. The two models are incorporated in a mathematical formulation that maximizes the expected revenues for each departure day and for each destination market. Our analysis provides a method for estimating choice behavior and passenger demand in response to RM strategies from readily available booking data. The accuracy of the estimates depends on the market size; for instance, the model produces good results for station market which is the predominant market for Acela Express. Overall, we show that the proposed model in this paper is promising and can potentially lead to increase in revenue.