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Optical Memory and Neural Networks

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01-01-2021

Short-Term Traffic Data Forecasting: A Deep Learning Approach

Author: A. A. Agafonov

Published in: Optical Memory and Neural Networks | Issue 1/2021

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Abstract

Accurate and timely traffic forecasting plays an important role in the development of intelligent transportation systems (ITS). Traffic data are the main information source for various tasks solved as part of the ITS, including traffic management, urban planning, route guidance, and others. Due to the spatial and temporal nonlinearity and complexity of traffic flow, traffic forecasting problem remains a subject of research. In this paper, we design a deep-learning framework that combines convolution operations on graph data with recurrent neural networks to solve the short-term traffic data forecasting problem. The proposed model takes into account recent, daily, and weekly periodic time series to capture different patterns in traffic flow. The experimental study of the model conducted on publicly available real-world datasets shows that the proposed model outperforms other baseline methods.

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Qureshi, K.N. and Abdullah, A.H., A survey on intelligent transportation systems, Middle East J. Sci. Res., 2013, vol. 15, no. 5, pp. 629–642. https://doi.org/10.5829/idosi.mejsr.2013.15.5.11215CrossRef

Patel, P., Narmawala, Z., and Thakkar, A., A survey on intelligent transportation system Using internet of things, Adv. Intell. Syst. Comput., 2019, vol. 882, pp. 231–240. https://doi.org/10.1007/978-981-13-5953-8_20CrossRef

Agafonov, A.A. and Yumaganov, A.S., Bus arrival time prediction using recurrent Neural Network with LSTM architecture, Opt. Mem. Neural Networks, 2019, vol. 28, no. 3, pp. 222–230. https://doi.org/10.3103/S1060992X19030081CrossRef

Agafonov, A. and Myasnikov, V., Stochastic on-time arrival problem with levy stable distributions, 2019, pp. 227–231. https://doi.org/10.1109/ICITE.2019.8880254

Vlahogianni, E.I., Karlaftis, M.G., and Golias, J.C., Short-term traffic forecasting: Where we are and where we’re going, Transp. Res., Part C: Emerging Technol., 2014, vol. 43, pp. 3–19. https://doi.org/10.1016/j.trc.2014.01.005CrossRef

Lana, I., Del Ser, J., Velez, M., and Vlahogianni, E.I., Road traffic forecasting: recent advances and new challenges, IEEE Intell. Transp. Syst. Mag., 2018, vol. 10, no. 2, pp. 93–109. https://doi.org/10.1109/MITS.2018.2806634CrossRef

Ahmed, M.S. and Cook, A.R., Analysis of freeway traffic time-series data by using Box-Jenkins techniques, Transp. Res. Rec., 1979, no. 722, pp. 1–9.

Williams, B.M. and Hoel, L.A., Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results, Transp. Eng. J., 2003, vol. 129, no. 6, pp. 664–672. https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)CrossRef

Chandra, S.R. and Al-Deek, H., Predictions of freeway traffic speeds and volumes using vector autoregressive models, J. Intell. Transp. Syst.: Technol., Plann. Oper., 2009, vol. 13, no. 2, pp. 53–72. https://doi.org/10.1080/15472450902858368CrossRef

10.

Sun, S., Zhang, C., and Yu, G., A Bayesian network approach to traffic flow forecasting, IEEE Trans. Intell. Transp. Syst., 2006, vol. 7, no. 1, pp. 124–133. https://doi.org/10.1109/TITS.2006.869623CrossRef

11.

Zheng, W., Lee, D.-H., and Shi, Q., Short-term freeway traffic flow prediction: Bayesian combined neural network approach, Transp. Eng. J., 2006, vol. 132, no. 2, pp. 114–121. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:2(114)CrossRef

12.

Wu, C.-H., Ho, J.-M., and Lee, D.T., Travel-time prediction with support vector regression, IEEE Trans. Intell. Transp. Syst., 2004, vol. 5, no. 4, pp. 276–281. https://doi.org/10.1109/TITS.2004.837813CrossRef

13.

Agafonov, A.A., Yumaganov, A.S., and Myasnikov, V.V., Big data analysis in a geoinformatic problem of short-term traffic flow forecasting based on a K nearest neighbors method, Comput. Opt., 2018, vol. 42, no. 6, pp. 1101–1111. https://doi.org/10.18287/2412-6179-2018-42-6-1101-1111CrossRef

14.

Tian, Y., Zhang, K., Li, J., Lin, X., and Yang, B., LSTM-based traffic flow prediction with missing data, Neurocomputing, 2018, vol. 318, pp. 297–305. https://doi.org/10.1016/j.neucom.2018.08.067CrossRef

15.

Xu, J., Rahmatizadeh, R., Boloni, L., and Turgut, D., Real-Time prediction of taxi demand using recurrent neural networks, IEEE Trans. Intell. Transp. Syst., 2018, vol. 19, no. 8, pp. 2572–2581. https://doi.org/10.1109/TITS.2017.2755684CrossRef

16.

Fu, R., Zhang, Z., and Li, L., Using LSTM and GRU neural network methods for traffic flow prediction, 2017, pp. 324–328. https://doi.org/10.1109/YAC.2016.7804912

17.

Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., and Liu, J., LSTM network: A deep learning approach for Short-term traffic forecast, IET Intell. Transp. Syst., 2017, vol. 11, no. 2, pp. 68–75. https://doi.org/10.1049/iet-its.2016.0208CrossRef

18.

Zhang, S., Tong, H., Xu, J., and Maciejewski, R., Graph convolutional networks: a comprehensive review, Comput. Soc. Networks, 2019, vol. 6, no. 1. https://doi.org/10.1186/s40649-019-0069-y

19.

Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y., Spectral networks and deep locally connected networks on graphs, 2nd Int. Conf. on Learning Representations, ICLR 2014—Conference Track Proceedings, 2014.

20.

Gao, H., Wang, Z., and Ji, S., Large-Scale Learnable Graph Convolutional Networks, 2018, pp. 1416–1424. https://doi.org/10.1145/3219819.3219947

21.

Kipf, T.N. and Welling, M., Semi-supervised classification with graph convolutional networks, 5th Int. Conf. on Learning Representations, ICLR 2017 – Conference Track Proceedings, 2019.

22.

Yu, B., Yin, H., and Zhu, Z., Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting, 2018, vol. 2018-July, pp. 3634–3640.

23.

Cui, Z., Henrickson, K., Ke, R., and Wang, Y., Traffic graph convolutional recurrent Neural Network: A deep learning framework for network-scale traffic learning and forecasting, IEEE Trans. Intell. Transp. Syst., 2019, pp. 1–12. https://doi.org/10.1109/TITS.2019.2950416

24.

Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H., Attention based spatial-temporal graph convolutional networks for traffic flow forecasting, AAAI, 2019, vol. 33, pp. 922–929. https://doi.org/10.1609/aaai.v33i01.3301922CrossRef

25.

Guo, G. and Yuan, W., Short-term traffic speed forecasting based on graph attention temporal convolutional networks, Neurocomputing, 2020, vol. 410, pp. 387–393. https://doi.org/10.1016/j.neucom.2020.06.001CrossRef

26.

Lu, Z., Lv, W., Cao, Y., Xie, Z., Peng, H., and Du, B., LSTM variants meet graph neural networks for road speed prediction, Neurocomputing, 2020, vol. 400, pp. 34–45. https://doi.org/10.1016/j.neucom.2020.03.031CrossRef

27.

Hammond, D.K., Vandergheynst, P., and Gribonval, R., Wavelets on graphs via spectral graph theory, Appl. Comput. Harmonic Anal., 2011, vol. 30, no. 2, pp. 129–150. https://doi.org/10.1016/j.acha.2010.04.005MathSciNetCrossRefMATH

28.

Hochreiter, S. and Schmidhuber, J., Long short-term memory, Neural Comput., 1997, vol. 9, no. 8, pp. 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735CrossRef

29.

Chen, C., Petty, K., Skabardonis, A., Varaiya, P., and Jia, Z., Freeway performance measurement system: Mining loop detector data, Transp. Res. Rec., 2001, no. 1748, pp. 96–102. https://doi.org/10.3141/1748-12

30.

Chollet, F., Keras, 2015. https://keras.io.

Title: Short-Term Traffic Data Forecasting: A Deep Learning Approach
Author: A. A. Agafonov
Publication date: 01-01-2021
Publisher: Pleiades Publishing
Published in: Optical Memory and Neural Networks / Issue 1/2021
Print ISSN: 1060-992X
Electronic ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X21010021

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