Abstract：
Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.
摘要：
每小时交通量,由自动交通记录仪(ATR)收集，是最重要的，因为它们被用来计算平均年每日交通量（AADT）和设计小时量（DHV）。因此，有必要确保收集数据的质量。不幸的是,ATR偶尔发生故障，导致数据缺失，以及不可靠的计数。这自然会影响从小时计数得出的关键参数的准确性。本研究旨在解决这一问题。来自新南威尔士州的ATR数据，对澳大利亚进行了违规行为和无效条目的筛选。随机选择了总共25％的可靠数据来测试13种不同的插补方法。数据遗漏的两种情况，即，25%和100%，进行了分析。结果表明，MissForest优于其他估算方法；因此，它被用来计算实际缺失的数据来完成数据集。AADT值是根据填补前的原始计数和填补后的完成计数计算得出的。来自估算数据的AADT值略高。绘制时的平均日体积验证了估算数据的质量，因为年度趋势显示出相对更好的拟合。
