Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (7): 241-247.DOI: 10.3778/j.issn.1002-8331.1712-0291

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Application of Deep Auto-Encoder Based on Sparse Theory in Highway Accident Casualty Forecast

ZHANG Wenjing, CHEN Zhiya, FENG Fenling, LI Wan   

  1. School of Traffic and Transportation, Central South University, Changsha 410075, China
  • Online:2019-04-01 Published:2019-04-15


张文婧,陈治亚,冯芬玲,李  万   

  1. 中南大学 交通运输工程学院,长沙 410075

Abstract: Accurate forecast of highway accident casualties is of great significance for the assurance of China’s future traffic safety situation and the realization of transportation system optimization. This paper innovatively takes Deep Auto-Encoder(DAE) based on the theory of sparse into the prediction of casualty. Then it constructs accident forecast model using casualty historical data of 2000—2015, and achieves that the average error rates are 1.69%, 1.53% of death toll and injury separately. This paper also constructs forecast model using the same period data of car ownership, GDP, the total mileage of the highway, road area per capita as the influence factors of casualty, and average error rates are 1.76%, 2.13% separately. By comparison, high precision model can be acquired by applying DAE into casualties prediction, and the accuracy of model based on casualty time series data is higher. So this paper uses it to forecast 2016—2020 data, the death toll will remain stable, while the injury will decline significantly.

Key words: deep learning, auto-encoder, artificial neural network, casualty forecast model, sparse theory

摘要: 准确实现公路事故伤亡人数预测,对于把握我国未来交通安全形势、实现运输系统优化具有重要意义。将基于稀疏理论的深度自动编码器(Deep Auto-encoder)引入公路事故伤亡人数预测,利用公路事故伤亡人数2000—2015年历史数据构建伤亡人数预测模型,得到死亡及受伤人数平均误差率分别为1.69%、1.53%;采用伤亡人数的影响指标汽车保有量、国内生产总值、公路总里程、人均道路面积的同时段历史数据构建预测模型,得到死伤人数平均误差率分别为1.76%、2.13%;对比发现将DAE运用到公路事故伤亡人数预测精度较高,且采用伤亡人数时间序列数据较影响指标预测精度更高,故使用前者对2016—2020年数据进行预测,得出未来我国公路事故死亡人数将在一定时间内保持平稳,而受伤人数将会明显下降。

关键词: 深度学习, 自编码, 人工神经网络, 伤亡预测模型, 稀疏理论