Date - Heure / Date - Hour
Date(s) - 26/11/2020
10h30 - 11h30
Airline Scheduling: learning from past errors
On time performance is a key attribute to attract airline’s customers. Airlines can control on time performance by enlarging schedule times and indeed airlines have been doing so over the last decades although this implies higher costs for their operations. For instance, according to Zhang et al (2018) scheduled duration increased by 8.1% between 1997 and 2017 in the case of US domestic flights. This article focuses on strategic decisions of airline scheduling. We argue that past on time performance (OTP) of the airlines will impact current decisions and that this impact may differ according to several factors such as time (day, time slot) or competition level.
Most of the literature studying delays and scheduling characteristics focus on scheduled time. Our article focuses on strategic choices by airlines and therefore analyzes buffer time rather than scheduled time. Scheduled time is affected by events that are not under the airline’s control such as ATM or airport characteristics. Buffer is defined as the difference between the scheduled flight time and the minimum time required to perform the flight, as defined by Mayer and Sinai (2003). In this sense, buffer time represents more accurately the airlines choices.
We analyze the schedule decisions by US domestic airlines over 4 million flights operated in August between 2008 and 2018. We study buffer choices at the flight level controlling for usual factors considered on the literature to affect scheduling decisions such as the competition level, the airport congestion or route and time fixed effects (day of the week or time slots). We differentiate the behavior of feeders and majors controlling for the relationship among them. Most importantly, the study considers how past delays affect current schedule decisions. We perform a two-stage least square regression to control for potential endogeneity from the competition measures.
We distinguish between positive and negative delays (flights arriving before scheduled time) and find that positive delays have a larger impact than negative delays. Flights suffering delays on the past increase buffer time by 1.35% on average compared to the previous year. Instead, flights with negative delays, corresponding to early arrival, reduce buffer time by 0.74% on average. Up to our knowledge this is the first time that past delays are exploited as explanatory variables for buffer decisions. Our results highlight that airlines have higher incentives to correct a bad OTP than to improve the customer’s average time expectations when they overperform.
This is joint work with Chantal Roucolle.
Mayer, Christopher, and Todd Sinai. 2003. “Network Effects, Congestion Externalities, and Air Traffic Delays: Or Why Not All Delays Are Evil.” American Economic Review 93 (4): 1194–1215. https://doi.org/10.1257/000282803769206269.
Zhang, Dennis, Yuval Salant, and Jan Albert Van Mieghem. 2018. “Where Did the Time Go? On the Increase in Airline Schedule Padding Over 21 Years.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3238457.
The presentation will be given in English.