Before and after study frameworks are widely adopted to evaluate the effectiveness of transportation policies and emerging technologies. However, many factors such as seasonal factors, holidays, and lane closure might interfere with the evaluation process by inducing variation in traffic volume during the before and after periods. In practice, limited effort has been made to eliminate the effects of these factors. In this study, an extreme gradient boosting (XGBoost)-based propensity score matching method is proposed to reduce the biases caused by traffic volume variation during the before and after periods. In order to evaluate the effectiveness of the proposed method, a corridor in the City of Chandler, Arizona where an advanced traffic signal control system has been recently implemented was selected. The results indicated that the proposed method is able to effectively eliminate the variation in traffic volume caused by the COVID-19 global Pandemic during the evaluation process. In addition, the results of the t-test and Kolmogorov-Smirnov (KS) test demonstrated that the proposed method outperforms other conventional propensity score matching methods. The application of the proposed method is also transferrable to other before and after evaluation studies and can significantly assist the transportation engineers to eliminate the impacts of traffic volume variation on the evaluation process.

翻译：前后对比研究框架被广泛用于评估交通政策与新兴技术的有效性。然而，季节性因素、节假日、车道封闭等多重因素可能因导致前后期间交通量的变化而干扰评估过程。实践中，消除这些因素影响的努力较为有限。本研究提出一种基于极端梯度提升（XGBoost）的倾向得分匹配方法，以降低前后期间交通量变化造成的偏差。为评估该方法的有效性，选取了美国亚利桑那州钱德勒市一条近期部署先进交通信号控制系统的走廊进行验证。结果表明，所提方法能有效消除评估过程中由COVID-19全球大流行引起的交通量波动。此外，t检验与柯尔莫哥洛夫-斯米尔诺夫检验（KS检验）的结果表明，该方法优于其他传统倾向得分匹配方法。本方法的适用性可迁移至其他前后对比评估研究，并能显著帮助交通工程师消除交通量变化对评估过程的影响。