首个公开的断电时段交通数据集，用于研究无信号交叉口的交通重建与控制。
Beacon: A Naturalistic Driving Dataset During Blackouts for Benchmarking Traffic Reconstruction and Control
- 采集孟菲斯两个路口4小时高峰时段断电期间车辆轨迹与流向数据。
- 引入机器人车可降低82.6%等待时间，但可能增加总碳排放。
- 适用于智能交通系统、自动驾驶与城市韧性研究者。
极端天气与基础设施脆弱性对城市交通构成重大挑战，尤其在信号失效的交叉口。为此，我们推出Beacon——一个自然驾驶数据集，记录美国田纳西州孟菲斯市两处主要交叉口在断电期间的交通动态。数据涵盖四小时高峰时段内每辆车的时间戳、起讫车道等详细交通行为。我们分析了不同场景下的交通需求、轨迹与密度，证明在无信号、有信号及混合交通条件下均可实现高保真重建。研究发现，引入机器人车辆（RVs）可显著降低交叉口延误，最高达82.6%；但车辆怠速减少可能导致整体二氧化碳排放上升。据我们所知，Beacon是首个公开的断电时段交叉口自然驾驶数据集。
原文摘要 · Abstract (English)
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.
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