Purchasing Behavior and Disruption Risks

In this thesis, we have treated passenger itineraries as if they are static, and attempted to optimize TFM allocation procedures around them. In truth, passenger purchasing behavior heavily influences the risk of itinerary disruptions. For example, consider a passenger choosing between flying through Chicago O’Hare (ORD) or Houston (IAH) to reach her final destination. In Table 3-11, we see that over 12% of passengers connecting through ORD had their itinerary disrupted as compared to under 6% for IAH. Thus, choosing the connection through ORD could easily double the risk of a disruption. But, the problem of incorporating disruption risks into itinerary selection is significantly more challenging than this alone. For example, if the connection time through ORD is 2 hours as compared to 30 minutes through IAH, it becomes less clear which choice will minimize the disruption risk.

There are four steps along this research path. The first is to use the estimated passenger itinerary flow data combined with historical flight performance data to develop a simplified stochastic model of the risk of disruption for one-stop itineraries. The second is to use this model of itinerary disruption risk to determine optimal purchasing decisions for different idealized classes of customers (e.g., risk-neutral, risk-adverse, high value of time, low value of time, etc.). The third is to analyze historical ticketing data to see what disruption risk factors, if any, have historically influenced passenger purchasing decisions. Our hypothesis is that due to lack of easily accessible information, passengers are unable to precisely differentiate these risks, instead relying on coarser purchasing rules. For example, though the passenger utility associated with connection times has been shown to vary, we believe it is likely the case that the flight delay characteristics of the connection airport are not appropriately accounted for. The last step is to perform a survey to test how presenting more complete information on itinerary disruption risks might influence purchasing decisions.

A nice property of this research plan is that it can be defended from one of two perspectives, either system efficiency or consumer advocacy. From a system efficiency perspective, the benefits should extend beyond the purchaser, because passenger purchasing behavior directly influences airline scheduling behavior. For example, a shift in passengers away from high-risk connections at congested airports would conceivably encourage further de-banking of highly congested hubs. The consumer advocacy case is even clearer, because better information on itinerary disruption risks would allow passengers to make more informed purchasing decisions.

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