Predicting Pedestrian–Vehicle Interaction Severity in Mixed Traffic Conditions: Feature-Based Long Short-Term Memory Neural Network Approach

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Novel model predicts pedestrian-vehicle interaction severity in mixed traffic, suggesting enhanced safety measures.

The model predicts pedestrian-vehicle interaction severity, improving traffic safety in urban areas.
Utilizing a dataset of over 4,300 interactions, the model categorizes events from Safe Passage to Conflict.
The LSTM model shows high accuracy in identifying Critical Events and enhances warning systems for accidents.
Robust performance across diverse traffic conditions indicates the model's potential for real-time traffic management.

Muduli et al. (2025) studied this question.

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