  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.
