Abstract
In recent decades, a vehicle crash has become a worldwide issue and labeled it the twelfth prominent reason for death worldwide. Artificial intelligence-based techniques, i.e., machine learning and deep learning play a vital role in various aspects of modern society and accidental predication is one of them. This research presents an accidental prediction model based on an improved deep neural network (IDNN). A DNN model mainly contains various hidden layers toward nodes. The proposed IDNN model includes two modules. The first model is based on an unsupervised feature learning interface to recognize operational networks and correlation factors. The second model is based on a supervised optimization subsystem with an extended negative binomial distribution which helps in forecasting road crashes. The proposed model was simulated over traffic datasets collected from the online data sources, and various performance measuring parameters have been calculated, i.e., precision, accidental detection. The experimental results demonstrate strengthen of the proposed model over the existing model.
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Simaiya, S., Lilhore, U.K., Pandey, H., Trivedi, N.K., Anand, A., Sandhu, J. (2022). An Improved Deep Neural Network-Based Predictive Model for Traffic Accident’s Severity Prediction. In: Hu, YC., Tiwari, S., Trivedi, M.C., Mishra, K.K. (eds) Ambient Communications and Computer Systems. Lecture Notes in Networks and Systems, vol 356. Springer, Singapore. https://doi.org/10.1007/978-981-16-7952-0_17
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