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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (8): 1613-1619    DOI: 10.3785/j.issn.1008-973X.2020.08.021
土木工程、交通工程     
基于双尺度长短期记忆网络的交通事故量预测模型
李文书(),邹涛涛,王洪雁,黄海
浙江理工大学 机器学习与智能系统团队，浙江 杭州 310018
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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摘要：

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    
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.
Key words: traffic accident    prediction model    long short-term memory network    dual-scale decomposition    dual-scale reconstruction
收稿日期: 2019-06-23 出版日期: 2020-08-28
CLC:  TP 181  
基金资助: 国家自然科学基金资助项目（31771224）；国家科技部重点研发计划重点专项课题资助项目（2018YFB1004901）；浙江省自然科学基金资助项目（LY16F020025，Y17C090031）
作者简介: 李文书（1975—）,男，教授，从事图像处理、认知建模研究. orcid.org/0000-0001-8339-8824. E-mail： charlie@zstu.edu.cn
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引用本文:

李文书,邹涛涛,王洪雁,黄海. 基于双尺度长短期记忆网络的交通事故量预测模型[J]. 浙江大学学报(工学版), 2020, 54(8): 1613-1619.

Wen-shu LI,Tao-tao ZOU,Hong-yan WANG,Hai HUANG. Traffic accident quantity prediction model based on dual-scale long short-term memory network. Journal of ZheJiang University (Engineering Science), 2020, 54(8): 1613-1619.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.08.021    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I8/1613

图 1  交通事故预测模型流程图
图 2  LSTM记忆块结构
epochs ACC/%
利兹数据集 UK数据集
400 74.8891 84.8406
500 79.3250 85.2503
600 79.9246 85.9217
700 81.5287 88.1876
800 82.0915 88.0292
900 83.2079 88.0172
1000 82.2952 85.4329
表 1  DS-LSTM模型在不同epochs下的准确度
图 3  epochs为900时的事故量（利兹数据集）
图 4  epochs为700时的事故量(UK数据集)
模型 UK，epochs=700 Leeds，epochs=900
MAE MSE RMSE ACC/% MAE MSE RMSE ACC/%
LSTM 0.5135 0.6161 0.7849 60.8588 0.4288 0.5811 0.7622 76.7608
GRU 0.6531 1.2837 1.1330 50.2231 1.0072 2.2285 1.4928 45.4190
SAEs 0.5872 0.8195 0.9052 55.2421 0.8611 1.4739 1.2140 53.3366
DS-LSTM 0.1900 0.0855 0.2924 88.1876 0.3227 0.2040 0.4517 83.2079
表 2  不同数据集下的模型特性
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