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| Traffic accident quantity prediction model based on dual-scale long short-term memory network |
Wen-shu LI( ),Tao-tao ZOU,Hong-yan WANG,Hai HUANG |
| Machine Learning and Intelligent Systems Team, Zhejiang University of Science and Technology, Hangzhou 310018, China |
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Abstract A novel traffic accident prediction model was proposed, in order to reduce the occurrence of traffic accidents and property losses. The dual-scale decomposition equations are utilized to decompose the original traffic accident time series into a number of sub-layers, and the long short-term memory (LSTM) network is adopted to complete the forecasting of the low-frequency sub-layer. The double scale reconstruction equations are adopted to complete the predicted value reconstruction of the low-frequency sub-layer. LSTM, gate recurrent unit (GRU), stacked autoencoders (SAEs) and dual-scale LSTM (DS-LSTM) prediciton models were constructed, and the four models were used to predict the two data sets. Results show that compared with other models, the proposed model is robust and more effective in predicting the traffic accident time series. Compared with the original LSTM model, the prediction accuracy of model DS_LSTM is improved by 6% and 28% respectively in the two data sets. Testing on two different databases (Leeds and UK) shows that the proposed model has better generalization performance than the other models involved.
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Received: 23 June 2019
Published: 28 August 2020
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基于双尺度长短期记忆网络的交通事故量预测模型
为了降低交通事故的发生、减少财产损失，建立新型交通事故量预测模型. 该模型利用双尺度分解方程将原始交通事故时间序列分解为多个子层，并利用长短期记忆（LSTM）网络对得到的低频子层进行预测；利用双尺度重构方程将低频子层的预测结果进行重构. 分别构建LSTM预测模型、门控循环单元(GRU)预测模型、自编码(SAEs)预测模型和双尺度长短期记忆网络(DS-LSTM)预测模型，利用这4个预测模型对2个数据集进行预测. 结果表明，本研究模型相较其他模型能够有效预测交通事故时间序列，且具有较强的鲁棒性. 对于2个数据集，相较于原始的LSTM模型，DS_LSTM预测模型预测准确度分别提高6%、28%；对2个不同数据库(利兹和UK)的测试表明本研究模型具有较好的泛化性能.
关键词：
交通事故,
预测模型,
长短期记忆网络,
双尺度分解,
双尺度重构
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