DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis
This work addresses pedestrian safety and urban planning challenges posed by autonomous vehicles, though it is incremental as it builds on existing survival analysis methods.
The study tackled pedestrian wait time estimation at unsignalized crosswalks in mixed traffic conditions by introducing DeepSurvival, a framework combining deep learning with Cox proportional hazard models, resulting in a C-index of 0.64 compared to 0.58 for a standard model.
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.