Debiased Machine Learning for Estimating the Causal Effect of Urban Traffic on Pedestrian Crossing Behavior

Abstract

Before the transition of automated vehicles (AVs) to urban roads and subsequently unprecedented changes in traffic conditions, the evaluation of transportation policies and futuristic road design related to pedestrian crossing behavior is of vital importance. Recent studies analyzed the non-causal impact of various variables on pedestrian waiting time in the presence of AVs. However, we mainly investigate the causal effect of traffic density on pedestrian waiting time. We develop a double/debiased machine learning (DML) model in which the impact of the confounders variable influencing both a policy and an outcome of interest is addressed, resulting in unbiased policy evaluation. Furthermore, we try to analyze the effect of traffic density by developing a copula-based joint model of the two main components of pedestrian crossing behavior, pedestrian stress level and waiting time. The copula approach has been widely used in the literature for addressing self-selection problems, which can be classified as a causality analysis in travel behavior modeling. The results obtained from copula approach and DML are compared based on the effect of traffic density. In the DML model structure, the standard error term of the density parameter is lower than that of the copula approach and the confidence interval is considerably more reliable. In addition, despite the similar sign of effect, the copula approach estimates the effect of traffic density lower than DML, because of the spurious effect of the confounders. In short, the DML model structure can flexibly adjust the impact of confounders by using machine learning algorithms and is more reliable for planning future policies.