Mitigating Landside Congestion at Airports through Predictive Control of Diversionary Messages

TL;DR

This paper introduces a data-driven predictive control framework to manage airport landside congestion by optimizing diversion messages, significantly improving traffic flow and reducing travel time based on real-world data at Seattle-Tacoma Airport.

Contribution

The paper develops a novel model predictive control approach using real-world data to optimize diversion messages for congestion mitigation at airports.

Findings

Up to threefold increase in roadway speed during congestion.

Savings of 20 to 80 vehicle-hours per hour of deployment.

Potential to significantly improve airport ground traffic efficiency.

Abstract

We present a data-driven control framework for adaptively managing landside congestion at airports. Ground traffic significantly impacts airport operations and critical efficiency, environmental, and safety metrics. Our framework models a real-world traffic intervention currently deployed at Seattle-Tacoma International Airport (SEA), where a digital signboard recommends drivers to divert to Departures or Arrivals depending on current congestion. We use measured vehicle flow/speed and passenger volume data, as well as time-stamped records of diversionary messages, to build a macroscopic system dynamics model. We then design a model predictive controller that uses our estimated dynamics to recommend diversions that optimize for overall congestion. Finally, we evaluate our approach on 50 real-world historical scenarios at SEA where no diversions were deployed despite significant congestion. Our results suggest that our framework would have improved speed in the congested roadway by up to three times and saved between 20 and 80 vehicle-hours of cumulative travel time in every hour of deployment. Overall, our work emphasizes the opportunity of algorithmic decision-making to augment operator judgment and intuition and yield better real-world outcomes.