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Before and after study frameworks are widely adopted to evaluate theeffectiveness of transportation policies and emerging technologies. However,many factors such as seasonal factors, holidays, and lane closure mightinterfere with the evaluation process by inducing variation in traffic volumeduring the before and after periods. In practice, limited effort has been madeto eliminate the effects of these factors. In this study, an extreme gradientboosting (XGBoost)-based propensity score matching method is proposed to reducethe biases caused by traffic volume variation during the before and afterperiods. In order to evaluate the effectiveness of the proposed method, acorridor in the City of Chandler, Arizona where an advanced traffic signalcontrol system has been recently implemented was selected. The resultsindicated that the proposed method is able to effectively eliminate thevariation in traffic volume caused by the COVID-19 global Pandemic during theevaluation process. In addition, the results of the t-test andKolmogorov-Smirnov (KS) test demonstrated that the proposed method outperformsother conventional propensity score matching methods. The application of theproposed method is also transferrable to other before and after evaluationstudies and can significantly assist the transportation engineers to eliminatethe impacts of traffic volume variation on the evaluation process.

A Causal Inference Approach to Eliminate the Impacts of Interfering Factors on Traffic Performance Evaluation