华东师范大学学报(自然科学版) ›› 2017, Vol. 2017 ›› Issue (2): 61-68.doi: 10.3969/j.issn.1000-5641.2017.02.008

• 计算机科学 • 上一篇    下一篇

改进BP神经网络在交通事故预测中的研究

陈海龙1, 彭伟2   

  1. 1. 华东师范大学 计算中心, 上海 200062;
    2. 华东师范大学 信息化办公室, 上海 200062
  • 收稿日期:2016-03-08 出版日期:2017-03-25 发布日期:2017-03-23
  • 通讯作者: 彭伟,男,高级工程师,硕士生导师,研究方向为计算机网络、Web应用技术及计算模拟.E-mail:wpeng@admin.ecnu.edu.cn E-mail:wpeng@admin.ecnu.edu.cn
  • 作者简介:陈海龙,男,硕士研究生,研究方向为物联网.E-mail:861137526@qq.com

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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摘要：

交通事故严重程度受多种因素的影响，适合用人工神经网络来建模预测.因为标准BP（Back Propagation，BP）神经网络具有收敛较慢的缺陷，所以在自适应学习和附加动量因子改进BP神经网络[1]基础上做了进一步的优化改进，使附加动量因子也具有自学习性.利用改进后的BP神经网络算法，选取英国利兹市的公开交通事故数据集，用影响交通事故严重程度的多种影响因素和事故严重程度构建并训练神经网络，并对最新数据进行预测.通过大量的实验对比收敛速度和预测结果，验证了改进后的算法具有更快的收敛速度和更高的预测准确率.

关键词: BP神经网络, 动量因子, 自学习, 交通事故

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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