Spatiotemporal Traffic Flow Prediction with KNN and LSTM
The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined wi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2019/4145353 |
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| _version_ | 1832563726508621824 |
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| author | Xianglong Luo Danyang Li Yu Yang Shengrui Zhang |
| author_facet | Xianglong Luo Danyang Li Yu Yang Shengrui Zhang |
| author_sort | Xianglong Luo |
| collection | DOAJ |
| description | The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement. |
| format | Article |
| id | doaj-art-b805d298b8c94401afd935a6d68c7c89 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b805d298b8c94401afd935a6d68c7c892025-02-03T01:12:48ZengWileyJournal of Advanced Transportation0197-67292042-31952019-01-01201910.1155/2019/41453534145353Spatiotemporal Traffic Flow Prediction with KNN and LSTMXianglong Luo0Danyang Li1Yu Yang2Shengrui Zhang3School of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaThe traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.http://dx.doi.org/10.1155/2019/4145353 |
| spellingShingle | Xianglong Luo Danyang Li Yu Yang Shengrui Zhang Spatiotemporal Traffic Flow Prediction with KNN and LSTM Journal of Advanced Transportation |
| title | Spatiotemporal Traffic Flow Prediction with KNN and LSTM |
| title_full | Spatiotemporal Traffic Flow Prediction with KNN and LSTM |
| title_fullStr | Spatiotemporal Traffic Flow Prediction with KNN and LSTM |
| title_full_unstemmed | Spatiotemporal Traffic Flow Prediction with KNN and LSTM |
| title_short | Spatiotemporal Traffic Flow Prediction with KNN and LSTM |
| title_sort | spatiotemporal traffic flow prediction with knn and lstm |
| url | http://dx.doi.org/10.1155/2019/4145353 |
| work_keys_str_mv | AT xianglongluo spatiotemporaltrafficflowpredictionwithknnandlstm AT danyangli spatiotemporaltrafficflowpredictionwithknnandlstm AT yuyang spatiotemporaltrafficflowpredictionwithknnandlstm AT shengruizhang spatiotemporaltrafficflowpredictionwithknnandlstm |