Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
•A Long Short-Term Memory Neural Network (LSTM) is developed for travel speed prediction.•The LSTM NN can capture the long-term temporal dependency for time series.•The LSTM NN can automatically determine the optimal time window.•A comparative study suggests that the LSTM NN receives the best perfor...
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| Published in | Transportation research. Part C, Emerging technologies Vol. 54; pp. 187 - 197 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier India Pvt Ltd
01.05.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | •A Long Short-Term Memory Neural Network (LSTM) is developed for travel speed prediction.•The LSTM NN can capture the long-term temporal dependency for time series.•The LSTM NN can automatically determine the optimal time window.•A comparative study suggests that the LSTM NN receives the best performance.
Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability. |
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| AbstractList | Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability. •A Long Short-Term Memory Neural Network (LSTM) is developed for travel speed prediction.•The LSTM NN can capture the long-term temporal dependency for time series.•The LSTM NN can automatically determine the optimal time window.•A comparative study suggests that the LSTM NN receives the best performance. Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability. |
| Author | Tao, Zhimin Wang, Yinhai Yu, Haiyang Ma, Xiaolei Wang, Yunpeng |
| Author_xml | – sequence: 1 givenname: Xiaolei surname: Ma fullname: Ma, Xiaolei email: xiaolei@buaa.edu.cn organization: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China – sequence: 2 givenname: Zhimin surname: Tao fullname: Tao, Zhimin email: msetao@126.com organization: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China – sequence: 3 givenname: Yinhai surname: Wang fullname: Wang, Yinhai email: yinhai@uw.edu organization: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States – sequence: 4 givenname: Haiyang surname: Yu fullname: Yu, Haiyang email: hyyu@buaa.edu.cn organization: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China – sequence: 5 givenname: Yunpeng surname: Wang fullname: Wang, Yunpeng email: ypwang@buaa.edu.cn organization: School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China |
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| Snippet | •A Long Short-Term Memory Neural Network (LSTM) is developed for travel speed prediction.•The LSTM NN can capture the long-term temporal dependency for time... Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks,... |
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| SubjectTerms | Algorithms Detectors Long short-term neural network Mathematical models Neural networks Nonlinear dynamics Remote microwave detector data Time lag Traffic engineering Traffic flow Traffic speed prediction |
| Title | Long short-term memory neural network for traffic speed prediction using remote microwave sensor data |
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