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Traffic Accident Modeling and Prediction Algorithm Using Convolutional Recurrent Neural Networks

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Advances in IoT and Security with Computational Intelligence (ICAISA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 756))

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Abstract

Predicting traffic accidents is a critical issue for enhancing transportation, public safety, and also securing routes. This issue has been often complicated by the rarity of accidents in space and time, as well as the environment's spatial variety (e.g., urban vs. rural). Many earlier domains of research on traffic accident prediction merely applied traditional predictive models to restricted information without effectively tackling the aforesaid barriers, resulting in disappointing results. So, here we suggest a traffic accident prediction model as well as an analytical framework relying on Convolutional Recurrent Neural Networks (CRNNs). We have employed a UK traffic accident dataset in which it was preprocessed and trained by utilizing CRNN. Various convolution kernels are in charge of extracting different features and new variables, which are then fed into the edge computing server's constructed training model for training and testing purpose. Modeling and estimating the probability of road accidents, develo** traffic accident risk alerting guidelines and transmitting real-time warning notifications to vehicle units are addressed. By altering the driver's driving condition in real-time, the driver can prevent traffic fatalities. We compared our proposed model (CRNN) to other models including Long Short-Term Memory (LSTM), DenseNet, ResNet, Visual Geometry Group (VGG), and Recurrent Neural Networks (RNNs) on performance metrics such as sensitivity, specificity, accuracy, F-score, and memory utilization, and found that our model provides improved satisfaction in terms of accuracy (95.10%) and memory utilization (88%).

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Author information

Authors and Affiliations

  1. Galgotias University, Greater Noida, India

    Anil Kumar & Shiv Kumar Verma

  2. Deen Dayal Upadhyaya College, University of Delhi, Delhi, India

    Anil Kumar

  3. Marwadi University, Rajkot, Gujarat, India

    Subhanshu Goyal

Authors
  1. Anil Kumar
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  2. Shiv Kumar Verma
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  3. Subhanshu Goyal
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Corresponding author

Correspondence to Anil Kumar .

Editor information

Editors and Affiliations

  1. Department of Electronics, Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India

    Anurag Mishra

  2. Department of Computer Science and Engineering, MNNIT Allahabad, Prayagraj, India

    Deepak Gupta

  3. Faculty of Science and Technology, University of Canberra, Bruce, ACT, Australia

    Girija Chetty

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Kumar, A., Verma, S.K., Goyal, S. (2023). Traffic Accident Modeling and Prediction Algorithm Using Convolutional Recurrent Neural Networks. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 756. Springer, Singapore. https://doi.org/10.1007/978-981-99-5088-1_30

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