Traffic accident monitoring system using deep learning

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    A short time period in development of rural places and public vehicle transportation system globally increased. The road accident are increased by the traffic problems last five years. It is a big problem of human society. These traffic accident are how can we happen and how to solve traffic management. Here we collect the traffic accident data and GPS record data using these data to build a deep learning model of stochastic gradient descent learning algorithm method used to solve critical problem of a traffic accident risk. 

      

     

  • Keywords


    Deep learning, GPS, stochastic gradient descent.

  • References


      [1] Eagle N, Pentland AS & Lazer D, “Inferring friendship network structure by using mobile phone data”, Proceedings of the National Academy of Sciences, Vol.106, No.36, (2009), pp.15274–15278.

      [2] Fan Z, Song X & Shibasaki R, “Cityspectrum: a non-negative tensor factorization approach”, Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, (2014), pp.213–223.

      [3] Grover A, Kapoor A & Horvitz E, “A deep hybrid model for weather forecasting”, Proceedings of the21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), pp.379–386.

      [4] Hinton GE & Salakhutdinov RR, ‘Reducing the dimensionality of data with neural networks”, Science, Vol.313, No.5786, (2006), pp.504–507.

      [5] Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN & Kingsbury B, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups”, Signal Processing Magazine, Vol.29, No.6, (2012), pp.82–97.

      [6] Hinton GE, Osindero S & The YW, “A fast learning algorithm for deep belief nets”, Neural computation, Vol.18, No.7, (2006), pp.1527–1554.

      [7] Huang W, Song G, Hong H & Xie K, “Deep architecture for traffic flow prediction: Deep belief networks with multitask learning”, IEEE Transactions on Intelligent Transportation Systems, (2014).


 

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Article ID: 12382
 
DOI: 10.14419/ijet.v7i2.21.12382




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