Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control

TL;DR

Beacon is a pioneering dataset capturing naturalistic driving behavior during blackouts at intersections, enabling benchmarking of traffic reconstruction and control strategies under extreme conditions.

Contribution

We introduce Beacon, the first public dataset of traffic during blackouts, providing detailed vehicle trajectories and analysis for benchmarking traffic management solutions.

Findings

Robot vehicles significantly reduce intersection delays by up to 82.6%.

Traffic reconstruction is highly accurate under various traffic conditions.

Environmental impacts vary with traffic control strategies, affecting CO2 emissions.

Abstract

Extreme weather and infrastructure vulnerabilities pose significant challenges to urban mobility, particularly at intersections where signals become inoperative. To address this growing concern, we introduce Beacon, a naturalistic driving dataset capturing traffic dynamics during blackouts at two major intersections in Memphis, TN, USA. The dataset provides detailed traffic movements, including timesteps, origin, and destination lanes for each vehicle over four hours of peak periods. We analyze traffic demand, vehicle trajectories, and density across different scenarios, demonstrating high-fidelity reconstruction under unsignalized, signalized, and mixed traffic conditions. We find that integrating robot vehicles (RVs) into traffic flow can substantially reduce intersection delays, with wait time improvements of up to 82.6%. However, this enhanced traffic efficiency comes with varying environmental impacts, as decreased vehicle idling may lead to higher overall CO2 emissions. To the best of our knowledge, Beacon is the first publicly available traffic dataset for naturalistic driving behaviors during blackouts at intersections.