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

arXiv: https://arxiv.org/abs/2212.11322 · HTML: https://mlsanity.com/paper/2212.11322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Double/Debiased/Neyman Machine Learning of Treatment Effects](https://mlsanity.com/paper/1701.08687/) (2017)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [Orthogonal Random Forest for Causal Inference](https://mlsanity.com/paper/1806.03467/) (2018)
- [Virtual Immersive Reality for Stated Preference Travel Behaviour Experiments: A Case Study of Autonomous Vehicles on Urban Roads](https://mlsanity.com/paper/1802.06180/) (2018)
- [Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices](https://mlsanity.com/paper/2204.09187/) (2022)
- [Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning](https://mlsanity.com/paper/2002.07325/) (2020)
- [Analysis of pedestrian stress level using GSR sensor in virtual immersive reality](https://mlsanity.com/paper/2111.11492/) (2021)
- [Ordered-logit pedestrian stress model for traffic flow with automated vehicles](https://mlsanity.com/paper/2204.11367/) (2022)

## Built on by (most cited first, 1 of 1)

- [A deep causal inference model for fully-interpretable travel behaviour analysis](https://mlsanity.com/paper/2405.01708/) (2024)

## Abstract (arXiv)

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.