  Before the transition of AVs to urban roads and subsequently unprecedented
changes in traffic conditions, 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
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 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 DML model
structure, the standard error term of density parameter is lower than 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, due to the spurious effect of
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.
