Real-time crash risk prediction on arterials based on LSTM-CNN.

By Pei Li, Mohamed A. Abdel-Aty, Jinghui Yuan. Accident Analysis and Prevention, 2019

367 citations recorded by the source corpus.

Real-time crash risk prediction is expected to play a crucial role in preventing traffic accidents. However, most existing studies only focus on freeways rather than urban arterials. This paper proposes a real-time crash risk prediction model on arterials using a long short-term memory convolutional neural network (LSTM-CNN). This model can explicitly learn from the various features, such as traffic flow characteristics, signal timing, and weather conditions. Specifically, LSTM captures the long-term dependency while CNN extracts the time-invariant features. The synthetic minority over-sampling technique (SMOTE) is used for resampling the training dataset. Five common models are developed to compare the results with the proposed model, such as the XGBoost, Bayesian Logistics Regression, LSTM, etc. Experiments suggest that the proposed model outperforms others in terms of Area Under the Curve (AUC) value, sensitivity, and false alarm rate. The findings of this paper indicate the promising performance of using LSTM-CNN to predict real-time crash risk on arterials.

Subfields: Building and Construction, Control and Systems Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/868b383c83a545b4940cbe4cf0ec44d0e0ba8b82

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

  1. Rahim Barzegar
  2. Jan Adamowski
  3. Mohammad Taghi Aalami

In Control and Systems Engineering

  1. Rahim Barzegar
  2. Jan Adamowski
  3. Mohammad Taghi Aalami

In Safety, Risk, Reliability and Quality

  1. Rahim Barzegar
  2. Jan Adamowski
  3. Mohammad Taghi Aalami

Citations in the corpus