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We apply Bayesian Linear Regression to estimate the response rate of driversto variable message signs at Seattle-Tacoma International Airport, or SeaTac.Our approach uses vehicle speed and flow data measured at the entrances of thearrival and departure-ways of the airport terminal, and sign message data.Depending on the time of day, we estimate that between 5.5 and 9.1% of driversdivert from departures to arrivals when the sign reads departures full, usearrivals, and conversely, between 1.9 and 4.2% of drivers divert from arrivalsto departures. Though we lack counterfactual data (i.e., what would havehappened had the diversionary treatment not been active), adopting a causalmodel that encodes time dependency with prior distributions rate can yield ameasurable effect.

Estimating Driver Response Rates to Variable Message Signage at Seattle-Tacoma International Airport