Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning

Haitao ZHAO; Huiling CHENG; Yi DING; Hui ZHANG; Hongbo ZHU

doi:10.11999/JEIT190595

Volume 42 Issue 1

Jan. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(1): 50-57

Haitao ZHAO, Huiling CHENG, Yi DING, Hui ZHANG, Hongbo ZHU. Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57. doi: 10.11999/JEIT190595

Citation:

Haitao ZHAO, Huiling CHENG, Yi DING, Hui ZHANG, Hongbo ZHU. Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2020, 42(1): 50-57. doi: 10.11999/JEIT190595

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Research on Traffic Accident Risk Prediction Algorithm of Edge Internet of Vehicles Based on Deep Learning

doi: 10.11999/JEIT190595

1.
Ministry of Education Ubiquitous Network Health Service System Engineering Research Center, Nanjing 210003, China
2.
Jiangsu Key Wireless Communication Laboratory, Nanjing 210003, China
3.
College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: The National Natural Science Foundation of China (61771252), The Natural Science Foundation Project of Jiangsu Province (BK20171444), The Jiangsu Province University Natural Science Research Major Project (18KJA510005), “The Six talents High Peaks” Class B Funding Project of Jiangsu Province (DZXX-041), The Jiangsu Provincial Association for Science and Technology Talents Entrustment Project, Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX19_0949)

Received Date: 2019-08-06
Rev Recd Date: 2019-11-05

Available Online: 2019-11-13

Publish Date: 2020-01-21

Abstract

Abstract

For the problem that the traditional traffic accident risk prediction algorithm can not automatically discriminate data features, and the model expression ability is poor, a traffic accident risk prediction algorithm based on deep learning is proposed. The algorithm firstly extracts multi-dimensional features by using the convolutional neural network established in the edge server for a large amount of traffic data collected in the edge network of vehicles. After normalization, de-equalization and other pre-processing, the new variables are input into the convolutional layer and the pooling layer for training. Finally, based on the output discrimination value of the fully connected layer, the risk of traffic accidents can be predicted by simulation. The simulation results show that the algorithm is validated to predict the risk of traffic accidents, and has lower loss and higher prediction accuracy than the traditional machine learning BP neural network algorithm and Logical Regression algorithm.
- Edge network of vehicles,
- Machine learning,
- Convolutional neural network,
- Edge server

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References(16)

References

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