Multi-class real-time crash risk forecasting using convolutional neural
  network: Istanbul case study
Behnaz Alafi Saeid Moradi
The performance of an artificial neural network (ANN) in forecasting crash
risk is shown in this paper. To begin, some traffic and weather data are
acquired as raw data. This data is then analyzed, and relevant characteristics
are chosen to utilize as input data based on additional tree and Pearson
correlation. Furthermore, crash and non-crash time data are separated; then,
feature values for crash and non-crash events are written in three four-minute
intervals prior to the crash and non-crash events using the average of all
available values for that period. The number of non-crash samples was lowered
after calculating crash likelihood for each period based on accident labeling.
The proposed CNN model is capable of learning from recorded, processed, and
categorized input characteristics such as traffic characteristics and
meteorological conditions. The goal of this work is to forecast the chance of a
real-time crash based on three periods before events. The area under the curve
(AUC) for the receiver operating characteristic curve (ROC curve), as well as
sensitivity as the true positive rate and specificity as the false positive
rate, are shown and compared with three typical machine learning and neural
network models. Finally, when it comes to the error value, AUC, sensitivity,
and specificity parameters as performance variables, the executed model
outperforms other models. The findings of this research suggest applying the
CNN model as a multi-class prediction model for real-time crash risk
prediction. Our emphasis is on multi-class prediction, while prior research
used this for binary (two-class) categorization like crash and non-crash.