用交通视频估算城市黑碳浓度，助力环保决策
Estimating Black Carbon Concentration from Urban Traffic Using Vision-Based Machine Learning
- 通过分析交通视频提取车辆行为特征，结合天气数据预测黑碳
- 街道路段黑碳预测准确率R²达0.72，均方误差129.42 ng/m³
- 适合城市规划、环保政策与环境公平研究者使用
城市黑碳排放主要来自交通，尤其在主干道附近形成热点，对弱势群体影响更大。由于黑碳监测依赖昂贵专业设备，缺乏本地交通源的实时数据，难以支持针对性政策。相比之下，交通监控系统在全球城市广泛部署，凸显了交通状况与环境影响认知间的巨大差距。为此，我们提出一种基于视觉的机器学习系统，从交通视频中提取车辆行为与状态信息，结合气象数据，在街道尺度上估算黑碳浓度，实现R²为0.72、均方根误差（RMSE）为129.42 ng/m³的预测性能。该方法利用现有城市基础设施和成熟建模技术，生成有助于交通减排、城市规划、公共健康与环境正义的可行动数据。
原文摘要 · Abstract (English)
Black carbon (BC) emissions in urban areas are primarily driven by traffic, with hotspots near major roads disproportionately affecting marginalized communities. Because BC monitoring is typically performed using costly and specialized instruments. there is little to no available data on BC from local traffic sources that could help inform policy interventions targeting local factors. By contrast, traffic monitoring systems are widely deployed in cities around the world, highlighting the imbalance between what we know about traffic conditions and what do not know about their environmental consequences. To bridge this gap, we propose a machine learning-driven system that extracts visual information from traffic video to capture vehicles behaviors and conditions. Combining these features with weather data, our model estimates BC at street level, achieving an R-squared value of 0.72 and RMSE of 129.42 ng/m3 (nanogram per cubic meter). From a sustainability perspective, this work leverages resources already supported by urban infrastructure and established modeling techniques to generate information relevant to traffic emission. Obtaining BC concentration data provides actionable insights to support pollution reduction, urban planning, public health, and environmental justice at the local municipal level.
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