Long short-term memory neural network for traffic speed prediction using remote microwave sensor data |
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| Institution: | 1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure, Systems, and Safety Control, Beihang University, Beijing 100191, China;2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, SiPaiLou #2, Nanjing 210096, China;3. Department of Science and Technology, Beijing Traffic Management Bureau, Beijing 100037, China;4. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195-2700, United States;1. Nextrans Center, Purdue University, West Lafayette, IN 47906, USA;2. School of Transportation, Southeast University, Nanjing, China;1. Intelligent Transportation System Research Center, Southeast University, Si Pai Lou #2, Nanjing 210096, PR China;2. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA;1. Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;2. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, China |
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| Abstract: | 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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| Keywords: | Neural networks Long short-term neural network Traffic speed prediction Remote microwave detector data |
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