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

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