用交通数据精准预测城市空气污染，实现动态高精度预报。
Tackling air quality with SAPIENS
- 将交通地图转为环形结构描述，更准确刻画交通状态。
- 基于墨西哥城数据，模型预测污染水平误差显著降低。
- 方法简单可复用，适合其他城市的实时空气质量预报。
空气污染是全球大城市长期存在的问题，车辆交通被证实是主要污染源之一。当前多数城市的空气质量监测与预报在时空上都较粗略，但实时交通流量数据通常公开且精细。本文以墨西哥城为例，深入研究了污染传感器数据与交通数据的关联，旨在实现超本地化、动态化的空气质量预测。提出一种新方法，将简单的彩色交通地图转化为同心环结构描述，以更有效地表征交通状况。采用偏最小二乘回归（PLS Regression）模型，基于此类新型交通强度指标预测污染物浓度。通过不同训练样本优化模型，提升预测性能，并揭示污染物与交通之间的关系。该流程设计简洁，具备跨城市可迁移性。
原文摘要 · Abstract (English)
Air pollution is a chronic problem in large cities worldwide and awareness is rising as the long-term health implications become clearer. Vehicular traffic has been identified as a major contributor to poor air quality. In a lot of cities the publicly available air quality measurements and forecasts are coarse-grained both in space and time. However, in general, real-time traffic intensity data is openly available in various forms and is fine-grained. In this paper, we present an in-depth study of pollution sensor measurements combined with traffic data from Mexico City. We analyse and model the relationship between traffic intensity and air quality with the aim to provide hyper-local, dynamic air quality forecasts. We developed an innovative method to represent traffic intensities by transforming simple colour-coded traffic maps into concentric ring-based descriptions, enabling improved characterisation of traffic conditions. Using Partial Least Squares Regression, we predict pollution levels based on these newly defined traffic intensities. The model was optimised with various training samples to achieve the best predictive performance and gain insights into the relationship between pollutants and traffic. The workflow we have designed is straightforward and adaptable to other contexts, like other cities beyond the specifics of our dataset.
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