Short-Term Travel Time Prediction: A Spatiotemporal Deep Learning Approach

Xiangdong Ran (), Zhiguang Shan, Yong Shi () and Chuang Lin ()
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Xiangdong Ran: School of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China
Zhiguang Shan: Informatization and Industry Development Department, State Information Center, Beijing 100045, P. R. China3College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, P. R. China
Yong Shi: School of Economic and Management, University of Chinese Academy of Sciences, Beijing 100049, P. R. China
Chuang Lin: Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 04, 1087-1111

Abstract: Traffic prediction is a complex, nonlinear spatiotemporal relationship modeling task with the randomness of traffic demand, the spatial and temporal dependency between traffic flows, and other recurrent and nonrecurrent factors. Based on the ability to learn generic features from history information, deep learning approaches have been recently applied to traffic prediction. Convolutional neural network (CNN) methods that learn traffic as images can improve the predictive accuracy by leveraging the implicit correlations among nearby links. Traffic prediction based on CNN is still in its initial stage without making full use of spatiotemporal traffic information. In this paper, we improve the predictive accuracy by directly capturing the relationship between the input sequence and the predicted value. We propose the new local receptive fields for spatiotemporal traffic information to provide the constraints in the task domain for CNN which is different from traditionally learning traffic as images. We explore a max-pooled CNN followed by a fully connected layer with a nonlinear activation function to convolute the new local receptive fields. The higher global-level features are fed into a predictor to generate the predicted output. Based on the dataset provided by Highways England, we validate the assumption that there exists direct relationship between the input sequence and the predicted value. We train the proposed method by using the backpropagation approach, and we employ the AdaGrad method to update the parameters of the proposed method. The experimental results show that the proposed method can improve the predictive accuracy.

Keywords: Deep learning; convolutional neural network; travel time prediction (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622019500202

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