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

Abstract

We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SeaTac. Our approach uses vehicle speed and flow data measured at the entrances of the arrival 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 drivers divert from departures to arrivals when the sign reads departures full, use arrivals, and conversely, between 1.9 and 4.2% of drivers divert from arrivals to departures. Though we lack counterfactual data (i.e., what would have happened had the diversionary treatment not been active), adopting a causal model that encodes time dependency with prior distributions rate can yield a measurable effect.