用机器学习选最佳采样日，提升交通量估算精度。
A Prescriptive Framework for Determining Optimal Days for Short-Term Traffic Counts
- 基于机器学习迭代选择最具代表性的短期采样日
- 最优日误差比传统方法降低30%以上，R²达0.9756
- 适合交通部门优化数据采集，降低监测成本
美国联邦公路管理局要求各州交通部门获取可靠的年均日交通量（AADT）数据。然而，许多州在未监控道路的准确测量上存在困难。虽然连续计数站能提供精确数据，但部署成本高、难以广泛覆盖，迫使机构依赖短期计数。本研究提出首个机器学习框架，用于确定最优代表性采样日，以提升AADT预测精度。基于德克萨斯州2022与2023年交通数据，对比两种情景：‘最优日’方法与当前多数州采用的‘无最优日’基线。利用连续计数数据模拟24小时短期计数，并通过留一法（LOO）技术生成无偏代表性特征。结果表明，最优日（第186天）在前五天中表现最佳，均方根误差（RMSE）为7,871.15，平均绝对误差（MAE）为3,645.09，平均绝对百分比误差（MAPE）为11.95%，决定系数（R²）达0.9756，显著优于基线（RMSE: 11,185.00, MAE: 5,118.57, MAPE: 14.42%, R²: 0.9499）。该研究为交通部门提供了替代传统短时计数的新方法，有助于满足高速公路绩效监测系统要求，降低全州交通数据采集的运营成本。
原文摘要 · Abstract (English)
The Federal Highway Administration (FHWA) mandates that state Departments of Transportation (DOTs) collect reliable Annual Average Daily Traffic (AADT) data. However, many U.S. DOTs struggle to obtain accurate AADT, especially for unmonitored roads. While continuous count (CC) stations offer accurate traffic volume data, their implementation is expensive and difficult to deploy widely, compelling agencies to rely on short-duration traffic counts. This study proposes a machine learning framework, the first to our knowledge, to identify optimal representative days for conducting short count (SC) data collection to improve AADT prediction accuracy. Using 2022 and 2023 traffic volume data from the state of Texas, we compare two scenarios: an 'optimal day' approach that iteratively selects the most informative days for AADT estimation and a 'no optimal day' baseline reflecting current practice by most DOTs. To align with Texas DOT's traffic monitoring program, continuous count data were utilized to simulate the 24 hour short counts. The actual field short counts were used to enhance feature engineering through using a leave-one-out (LOO) technique to generate unbiased representative daily traffic features across similar road segments. Our proposed methodology outperforms the baseline across the top five days, with the best day (Day 186) achieving lower errors (RMSE: 7,871.15, MAE: 3,645.09, MAPE: 11.95%) and higher R^2 (0.9756) than the baseline (RMSE: 11,185.00, MAE: 5,118.57, MAPE: 14.42%, R^2: 0.9499). This research offers DOTs an alternative to conventional short-duration count practices, improving AADT estimation, supporting Highway Performance Monitoring System compliance, and reducing the operational costs of statewide traffic data collection.
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