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

TL;DR

This paper introduces an XGBoost-based propensity score matching method to eliminate interference factors like seasonal effects and holidays, improving traffic performance evaluation accuracy during before-and-after studies.

Contribution

It proposes a novel machine learning approach to reduce biases from traffic volume variations, enhancing the reliability of transportation policy assessments.

Findings

Effectively removes traffic volume variation caused by COVID-19

Outperforms conventional propensity score matching methods

Applicable to various before-and-after evaluation studies

Abstract

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.