Design of a Novel Convolutional Deep Network Model for Car Accident Prediction

Abstract

Real-time collision risk estimation is thought to be essential to a sophisticated traffic management system. To swiftly determine accident probability is the goal of real-time crash risk prediction.  However, due to the complex traffic situation on urban arterials, urban arterials were rarely included in previous studies, which mostly focused on highways. This paper suggests using Convolutional Deep Network model (CDNM) to forecast the probability of vascular accidents in real time.  This model has the benefit of being able to use both LSTM and CNN.  CNN retrieves the time-invariant characteristics, while LSTM captures the data's long-term dependability. To estimate the likelihood of an accident, many sorts of data are used, including weather, traffic, and signal timing data. There are also many other data preparation methods employed. The problem of data imbalance is also addressed by normalization which oversamples the crash cases. Using a variety of measures, the CDNM is enhanced on the training data and assessed on the test data.  Five more benchmark models are constructed for model comparison. K-NN, ISVM, ANN, CNN, CNN-EVT and GAN are some of the models in this group. Experimental findings show that the proposed CDNM beats the competition in terms of sensitivity, specificity, accuracy, AUC and G-mean value. The findings of this paper demonstrate that CDNM can real-time prediction of crash risk at arterials.