Abstract
Recently years, traffic prediction has become an important and challenging problem in smart urban traffic computing, which can be used for government for road planning, detecting bottle-neck congestions roads, pollution emissions estimating and so on. However, former data mining algorithms mainly address the problem by using the traditional mathematical or statistical theories, and they were impossible to model the spatial and temporal relationship simultaneously. To address these issues, we propose an end-to-end neural network named C-LSTM to predict the traffic congestion at next time interval. More specifically, the C-LSTM is based on CNN and LSTM to collectively capture the spatial-temporal dependencies on the road network. Inspired by the procedure of handling the image by CNN, the city-wide traffic maps are first converted into a series of static images like the video frame and then are fed into a deep learning architecture, in which CNN extracts the spatial characteristics, and LSTM extracts the temporal characteristics. In addition, we also consider some external factors to further improve the prediction accuracy. Extensive experiments on reality Beijing transportation datasets demonstrate the superiority of our method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105. Hinton (2012)
Shu, Y., Jin, Z., Zhang, L., et al.: Traffic prediction using FARIMA models. In: IEEE International Conference on Communications, ICC 1999, vol. 2, pp. 891–895. IEEE (1999)
Wang, D., Zhang, J., Cao, W., et al.: When will you arrive? Estimating travel time based on deep neural networks. In: AAAI (2018)
Yu, H., Wu, Z., Wang, S., et al.: Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7), 1501 (2017)
Yao, H., Wu, F., Ke, J., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. arXiv preprint arXiv:1802.08714 (2018)
Ke, J., Zheng, H., Yang, H., et al.: Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp. Res. Part C Emerg. Technol. 85, 591–608 (2017)
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655–1661 (2017)
Xingjian, S.H.I., Chen, Z., Wang, H., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810. MLA (2015)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)
Clark, S.: Traffic prediction using multivariate nonparametric regression. J. Transp. Eng. 129(2), 161–168 (2003)
Min, W., Wynter, L.: Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C Emerg. Technol. 19(4), 606–616 (2011)
Song, X., Kanasugi, H., Shibasaki, R.: DeepTransport: prediction and simulation of human mobility and transportation mode at a citywide level. In: IJCAI, vol. 16, pp. 2618–2624 (2016)
Liao, S., Zhou, L., Di, X., et al.: Large-scale short-term urban taxi demand forecasting using deep learning. In: Proceedings of the 23rd Asia and South Pacific Design Automation Conference, pp. 428–433. IEEE Press (2018)
Zhang, S., Wu, G., Costeira, J.P., et al.: FCN-rLSTM: Deep spatio-temporal neural networks for vehicle counting in city cameras. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3687–3696. IEEE (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ma, X., Tao, Z., Wang, Y., et al.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C Emerg. Technol. 54, 187–197 (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM (1999)
Wang, J., Gu, Q., Wu, J., et al.: Traffic speed prediction and congestion source exploration: a deep learning method. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 499–508. IEEE (2016)
Zheng, Y.: Methodologies for cross-domain data fusion: an overview. IEEE Trans. Big Data 1(1), 16–34 (2015)
Ta, N., Li, G., Zhao, T., et al.: An efficient ride-sharing framework for maximizing shared route. IEEE Trans. Knowl. Data Eng. (TKDE) 30, 219–233 (2017)
Tong, Y., Chen, Y., Zhou, Z., et al.: The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1653–1662. ACM (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Acknowledgments
This research was financially supported by NSFC (91646202), the National High-tech R&D Program of China (SS2015AA020102), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program, Tsinghua University Initiative Scientific Research Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xu, J., Zhang, Y., Jia, Y., Xing, C. (2019). An Efficient Traffic Prediction Model Using Deep Spatial-Temporal Network. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-12981-1_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12980-4
Online ISBN: 978-3-030-12981-1
eBook Packages: Computer ScienceComputer Science (R0)