DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous Vehiclesfeature selectionSurvival AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning
To ensure pedestrian friendly streets in the era of automated vehicles, reassessment of current policies, practices, design, rules and regulations of urban areas is of importance. This study investigates pedestrian cross…
Interpretable Machine LearningA Max Pressure Algorithm for Traffic Signals Considering Pedestrian Queues
This paper proposes a novel max-pressure (MP) algorithm that incorporates pedestrian traffic into the MP control architecture. Pedestrians are modeled as being included in one of two groups: those walking on sidewalks an…
Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices
This study presents an Ordinal version of Residual Logit (Ordinal-ResLogit) model to investigate the ordinal responses. We integrate the standard ResLogit model into COnsistent RAnk Logits (CORAL) framework, classified a…
Binary ClassificationregressionA novel pedestrian road crossing simulator for dynamic traffic light scheduling systems
The major advances in intelligent transportation systems are pushing societal services toward autonomy where road management is to be more agile in order to cope with changes and continue to yield optimal performance. Ho…
ManagementSchedulingDebiased machine learning for estimating the causal effect of urban traffic on pedestrian crossing behaviour
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 vit…