Abstract
In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but also in terms of testing accuracy.
This work was supported by the National Natural Science Foundation of China under Grant No. 61673107, the National Ten Thousand Talent Program for Young Top-notch Talents under Grant No. W2070082, the General joint fund of the equipment advance research program of Ministry of Education under Grant No. 6141A020223, and the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence under Grant No. BM2017002.
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
Similar content being viewed by others
Notes
- 1.
According to the documentation in https://www.mathworks.com/help/deeplearning/ref/train.html.
- 2.
Implemented in Matlab according to the documentation in https://www.mathworks.com/help/stats/lasso.html.
- 3.
The documentation for training a stacked autoencoder can be found in https://www.mathworks.com/help/deeplearning/examples/train-stacked-autoencoders-for-image-classification.html.
References
Ben-Israel, A., Greville, T.N.E.: Generalized Inverses: Theory and Applications. Wiley, New York (1974)
Chan, K.Y., Dillon, T.S., Singh, J., Chang, E.: Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg–marquardt algorithm. IEEE Trans. Intell. Transp. Syst. 13(2), 644–654 (2012)
Chen, C.L.P., Zhang, C.Y., Chen, L., Gan, M.: Fuzzy restricted boltzmann machine for the enhancement of deep learning. IEEE Trans. Fuzzy Syst. 23(6), 2163–2173 (2015)
Chen, C.P., Liu, Z.L.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018)
Chen, C.P., Wan, J.Z.: A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Trans. Syst. Man Cybern. Part B Cybern. 29(1), 62–72 (1999)
Chen, M., Yu, X., Liu, Y.: PCNN: deep convolutional networks for short-term traffic congestion prediction. IEEE Trans. Intell. Transp. Syst. 19(11), 1–10 (2018)
Ghofrani, F., Jamshidi, A., Keshavarz-Haddad, A.: Internet traffic classification using hidden naive Bayes model. In: Electrical Engineering, Tehran, pp. 235–240. IEEE Press (2015)
Gong, M., Liu, J., Li, H., Cai, Q., Su, L.: A multiobjective sparse feature learning model for deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3263–3277 (2017)
Hoerl, A., Kennard, R.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (2000)
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)
Igelnik, B., Pao, Y.H.: Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural Netw. 6(6), 1320–1329 (1995)
Koesdwiady, A., Soua, R., Karray, F.: Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Trans. Veh. Technol. 65(12), 9508–9517 (2016)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2015)
Pao, Y.H., Takefuji, Y.: Functional-link net computing: theory, system architecture, and functionalities. Computer 25(5), 76–79 (1992)
Polson, N.G., Sokolov, V.O.: Deep learning for short-term traffic flow prediction. Transp. Res. Part C Emerg. Technol. 79, 1–17 (2017)
Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2017)
Wu, Y., Tan, H., Qin, L., Ran, B., Jiang, Z.: A hybrid deep learning based traffic flow prediction method and its understanding. Transp. Res. Part C 90(1), 166–180 (2018)
Xu, M., Han, M., Chen, C.L.P., Qiu, T.: Recurrent broad learning systems for time series prediction. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/TCYB.2018.2863020
Yang, H.F., Dillon, T.S., Chen, Y.P.: Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2371–2381 (2016)
Yi, H., Jung, H.J., Bae, S.: Deep neural networks for traffic flow prediction. In: IEEE International Conference on Big Data & Smart Computing, Jeju, pp. 328–331. IEEE Press (2017)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 316–324 (2011)
Zhan, H., Gomes, G., Li, X.S., Madduri, K., Wu, K.: Consensus ensemble system for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 19(12), 3903–3914 (2018)
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
Liu, D., Yu, W., Baldi, S. (2019). Broad Learning for Optimal Short-Term Traffic Flow Prediction. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)