Journal of East China Normal University(Natural Sc ›› 2017, Vol. 2017 ›› Issue (2): 61-68.doi: 10.3969/j.issn.1000-5641.2017.02.008

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Research on improved BP neural network in forecasting traffic accidents

CHEN Hai-long1, PENG Wei2   

  1. 1. Computer Center, East China Normal University, Shanghai 200062, China;
    2. Information Technology Services Center, East China Normal University, Shanghai 200062, China
  • Received:2016-03-08 Online:2017-03-25 Published:2017-03-23

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Abstract:

The traffic accident severity is affected by many factors. It is suitable for modeling and forecasting by using the artificial neural network (ANN). Because standard BP(back propagation) neural network has the defect of slow convergence, based on the improved BP neural network with adaptive learning and additional momentum factor[1], so the additional momentum factor was made to be self-learning for further optimization and improvement. Using the improved BP neural network algorithm, the public traffic accident data set in Leeds of England was selected to construct and train the neural network to predict the latest records. The data set includes many kinds of influencing factors and accident severity. After a lot of experiments, by comparing the convergence rate and prediction results, it has been proved that the improved algorithm has faster convergence rate and higher forecasting accuracy rate.

Key words: BP neural network, momentum factor, self-learning, traffic accident

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