用随机森林预测道路车流量区间，更准更可靠。
Interval Prediction of Annual Average Daily Traffic on Local Roads via Quantile Random Forest with High-Dimensional Spatial Data
- 结合主成分分析与分位数随机森林，生成车流量预测区间。
- 在2000多条道路数据上，区间覆盖率88.22%，宽度合理。
- 适合交通规划、基础设施管理等需评估不确定性的场景。
准确的年均日交通量（AADT）数据对交通规划和基础设施管理至关重要。然而，全国路网中的自动交通检测器覆盖不全，导致次要道路数据严重缺失。尽管近年来机器学习提升了未监测点的AADT估计精度，但多数模型仅输出点预测，忽略不确定性。本文提出一种区间预测方法，显式量化预测不确定性。通过将分位数随机森林与主成分分析结合，生成基于最小值和最大值的车流量合理范围。基于英格兰与威尔士超过2000条次要道路的数据，使用专门的区间评估指标验证，该方法达到88.22%的区间覆盖率、0.23的归一化平均宽度以及7,468.47的Winkler分数。该框架融合机器学习与高维空间分析，显著提升AADT估计的准确性与可解释性，为更稳健的交通规划提供支持。
原文摘要 · Abstract (English)
Accurate annual average daily traffic (AADT) data are vital for transport planning and infrastructure management. However, automatic traffic detectors across national road networks often provide incomplete coverage, leading to underrepresentation of minor roads. While recent machine learning advances have improved AADT estimation at unmeasured locations, most models produce only point predictions and overlook estimation uncertainty. This study addresses that gap by introducing an interval prediction approach that explicitly quantifies predictive uncertainty. We integrate a Quantile Random Forest model with Principal Component Analysis to generate AADT prediction intervals, providing plausible traffic ranges bounded by estimated minima and maxima. Using data from over 2,000 minor roads in England and Wales, and evaluated with specialized interval metrics, the proposed method achieves an interval coverage probability of 88.22%, a normalized average width of 0.23, and a Winkler Score of 7,468.47. By combining machine learning with spatial and high-dimensional analysis, this framework enhances both the accuracy and interpretability of AADT estimation, supporting more robust and informed transport planning.
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