Road Traffic Accident Prediction Model Based on J-LSTM+Attention Mechanism - 文章详情页

作者：

Wang, Shunshun (Wang, Shunshun.) | Yan, Changshun (Yan, Changshun.) | Shao, Yong (Shao, Yong.)

收录：

EI Scopus

摘要：

In　order　to　accurately　predict　the　number　of　traffic　accidents　and　better　solve　road　safety　problems,　this　paper　presents　a　time　series　prediction　model　based　on　an　J-LSTM　+　Attention　mechanism,　using　road　traffic　accident　data　and　meteorological　data　from　the　city　of　CURITIBA,　Brazil,　as　the　research　object,　and　improving　the　internal　gating　unit　structure　of　the　LSTM　model.　The　traffic　accident　dataset　is　fitted　and　predicted.　The　results　show　that　the　prediction　effects　of　the　road　traffic　accident　prediction　model　based　on　the　J-LSTM　+　Attention　mechanism　are　all　better　than　those　of　the　classical　LSTM　model,　BP　neural　network　and　SVR　model,　and　the　overall　effect　of　the　model　is　better,　which　is　of　great　practical　significance　for　improving　road　traffic　management.　©　2023　IEEE.

关键词：

Highway accidents Meteorology Highway administration Motor transportation Long short-term memory Forecasting Roads and streets

作者机构：

[ 1 ] [Wang, Shunshun]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 2 ] [Yan, Changshun]Beijing University of Technology, Faculty of Information Technology, Beijing, China
[ 3 ] [Shao, Yong]Beijing University of Technology, Faculty of Information Technology, Beijing, China

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