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 to variable message signs at Seattle-Tacoma International Airport, or SEA. 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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Findings Press
2022-09-01
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| Series: | Findings |
| Online Access: | https://doi.org/10.32866/001c.38134 |
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| Summary: | We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SEA. 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. |
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| ISSN: | 2652-8800 |