# DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis

## TL;DR

DeepWait introduces a deep survival analysis framework to accurately estimate pedestrian waiting times at crosswalks in mixed traffic, leveraging deep learning for improved prediction and interpretability.

## Contribution

The paper presents DeepSurvival, a novel deep learning-based Cox model with feature selection for pedestrian wait time estimation in complex traffic scenarios.

## Key findings

- DeepWait achieved a C-index of 0.64, outperforming the standard model's 0.58.
- The framework effectively captures nonlinearities in pedestrian behavior data.
- Embedded feature selection enhances model interpretability.

## Abstract

Pedestrian's road crossing behaviour is one of the important aspects of urban dynamics that will be affected by the introduction of autonomous vehicles. In this study we introduce DeepSurvival, a novel framework for estimating pedestrian's waiting time at unsignalized mid-block crosswalks in mixed traffic conditions. We exploit the strengths of deep learning in capturing the nonlinearities in the data and develop a cox proportional hazard model with a deep neural network as the log-risk function. An embedded feature selection algorithm for reducing data dimensionality and enhancing the interpretability of the network is also developed. We test our framework on a dataset collected from 160 participants using an immersive virtual reality environment. Validation results showed that with a C-index of 0.64 our proposed framework outperformed the standard cox proportional hazard-based model with a C-index of 0.58.