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Data-driven crash prediction by injury severity using a recurrent neural network model based on Keras framework
Dajie Zuo1, Cheng Qian2, Daiquan Xiao3
1School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China.
Summary
This study introduces a data-driven recurrent neural network (RNN) model for predicting urban traffic injury severity. The model accurately forecasts injury levels using clustering and deep learning on crash data, offering valuable insights for road safety.
Area of Science:
- Traffic Safety
- Data Science
- Machine Learning
Background:
- Big data and deep learning technologies are increasingly utilized in data-driven applications.
- Predicting traffic injury severity is crucial for urban safety and resource allocation.
Purpose of the Study:
- To propose a recurrent neural network (RNN) model for predicting urban traffic injury severity.
- To leverage clustering and deep learning for enhanced prediction accuracy and speed.
Main Methods:
- Utilized OPTICS clustering algorithm on Nevada crash data (2014-2017) for Las Vegas.
- Developed a recurrent neural network (RNN) model using the Keras framework.
- Optimized model parameters including loss function, activation function, and optimizer.
Main Results:
- The RNN model achieved high accuracy in predicting injury severity.
- The model demonstrated a high training speed, suitable for real-time applications.
- Successful visualization of model training results was achieved.
Conclusions:
- The proposed data-driven RNN model offers an effective method for predicting urban traffic injury severity.
- The findings provide potential insights for improving road safety strategies.
- This approach demonstrates the power of machine learning in analyzing complex urban safety data.
