Outlier detection in network revenue management

TL;DR

This paper introduces a two-step clustering and outlier detection method for identifying demand outliers in network revenue management, improving forecast accuracy and supporting revenue optimization in complex, multi-leg networks.

Contribution

It proposes a novel combined clustering and functional outlier detection approach tailored for network demand data, enhancing outlier detection efficiency and accuracy.

Findings

Outperforms leg-only analysis in detecting demand outliers

Demonstrates robustness through simulation studies

Shows potential revenue benefits from demand forecast adjustments

Abstract

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pool booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn.