Debiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour

TL;DR

This paper develops a debiased machine learning approach to accurately estimate how traffic density causally affects pedestrian crossing behavior, improving policy evaluation reliability.

Contribution

It introduces a Double/Debiased Machine Learning model combined with a copula-based joint model to better assess traffic density effects while accounting for confounders.

Findings

DML provides lower standard errors and more reliable confidence intervals.

DML estimates a higher effect of traffic density than copula approach.

The approach enhances unbiased policy evaluation in urban traffic planning.

Abstract

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.