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

TL;DR

This study uses Bayesian Linear Regression to estimate how drivers at SeaTac respond to variable message signs, revealing diversion rates based on sign messages and time of day, despite lacking counterfactual data.

Contribution

The paper introduces a Bayesian approach to estimate driver response rates to variable message signs using real-time vehicle data at SeaTac.

Findings

5.5-9.1% of drivers divert from departures to arrivals when sign indicates full departures

1.9-4.2% of drivers divert from arrivals to departures based on sign messages

Bayesian model captures time-dependent response rates despite lacking counterfactual data

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.