Abstract
Traffic forecasting is an important component of the Intelligent Transportation System (ITS). Recently, deep learning has been introduced as a promising method for traffic forecasting to deal with the exponential growth of data in ITS. In this regard, this paper focuses on applying a deep neural network model using LSTM for traffic forecasting based on analyzing data from the Vehicle Detection System (VDS). In particular, we first try to understand the traffic condition by applying visualization techniques. Then, based on the traffic condition, we apply an appropriate deep learning model for predicting traffic flow. Experiments in a certain urban area present promising results by applying the proposed model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Bui, K.H.N., Jung, J.J.: Internet of agents framework for connected vehicles: a case study on distributed traffic control system. J. Parallel Distrib. Comput. 116, 89–95 (2018)
Bui, K.H.N., Jung, J.J.: Computational negotiation-based edge analytics for smart objects. Inf. Sci. 480, 222–236 (2019)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(1), 6085 (2018)
Chen, D.: Research on traffic flow prediction in the big data environment based on the improved RBF neural network. IEEE Trans. Ind. Inf. 13(4), 2000–2008 (2017)
Huang, W., Song, G., Hong, H., Xie, K.: Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15(5), 2191–2201 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, USA (2015). https://arxiv.org/abs/1412.6980
Lana, I., Ser, J.D., Vélez, M., Vlahogianni, E.I.: Road traffic forecasting: recent advances and new challenges. IEEE Intell. Transp. Syst. Mag. 10(2), 93–109 (2018)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: 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)
Nellore, K., Hancke, G.P.: A survey on urban traffic management system using wireless sensor networks. Sensors 16(2), 157 (2016)
Park, S.H., Kim, S.M., Ha, Y.G.: Highway traffic accident prediction using VDS big data analysis. J. Supercomput. 72(7), 2815–2831 (2016)
Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014), pp. 338–342. ISCA (2014)
Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transp. Res. Part C: Emerg. Technol. 43, 3–19 (2014)
Yi, H., Bui, K.N., Jung, H.: Implementing a deep learning framework for short term traffic flow prediction. In: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics (WIMS 2019), pp. 7:1–7:8. ACM (2019)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
Zhao, Z., Chen, W., Wu, X., Chen, P.C., Liu, J.: LSTM network: a deep learning approach for short-term traffic forecast. IET Intell. Transp. Syst. 11(2), 68–75 (2017)
Acknowledgment
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00494, Development of deep learning-based urban traffic congestion prediction and signal control solution system) and Korea Institute of Science and Technology Information (KISTI) grant funded by the Korea government (MSIT) (K-19-L02-C07-S01).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Yi, H., Bui, KH.N. (2019). VDS Data-Based Deep Learning Approach for Traffic Forecasting Using LSTM Network. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-30241-2_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30240-5
Online ISBN: 978-3-030-30241-2
eBook Packages: Computer ScienceComputer Science (R0)