Abstract
This paper presents a new approach for spatio-temporal road traffic forecasting that relies on the adoption of the NeuCube architecture based on spiking neural networks. The NeuCube platform was originally conceived and designed to process electroencephalographic (EEG) signals considering their temporal component and their spatial source within the brain. Its neural representation allows for a visual analysis of connectivity among different locations, and also provides a prediction tool harnessing the predictive learning capabilities of dynamic evolving Spiking Neural Networks (deSNNs). Taking advantage of the NeuCube features, this work focuses on the potential of spatially-aware traffic variable forecasts, as well as on the exploration of the spatio-temporal relationships among different sensor locations within a traffic network. Its performance, assessed over real traffic data collected in 51 locations in the center of Madrid (Spain), is superior to that of other machine learning techniques in terms of forecasting accuracy. Moreover, we discuss on the interactions and relationships among sensors of the network provided by Neucube, which may provide valuable insights on the traffic dynamics of the city under study towards enhancing its management.
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References
Abadi, A., Rajabioun, T., Ioannou, P.A.: Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans. Intell. Transp. Syst. 16(2), 653–662 (2015)
Ahmed, M.S, Cook, A.R.: Analysis of freeway traffic time-series data by using box-jenkins techniques, vol. 722 (1979)
Bose, P., Kasabov, N.K., Bruzzone, L., Hartono, R.N.: Spiking neural networks for crop yield estimation based on spatiotemporal analysis of image time series. IEEE Trans. Geosci. Remote. Sens. 54(11), 6563–6573 (2016)
Cai, P., Wang, Y., Lu, G., Chen, P., Ding, C., Sun, J.: A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transp. Res. Part C: Emerg. Technol. 62, 21–34 (2016)
Capano, V., Herrmann, H.J., de Arcangelis, L.: Optimal percentage of inhibitory synapses in multi-task learning. Sci. Rep. 5, 9895 (2015)
Capecci, E., Kasabov, N., Wang, G.Y.: Analysis of connectivity in neucube spiking neural network models trained on eeg data for the understanding of functional changes in the brain: a case study on opiate dependence treatment. Neural Netw. 68, 62–77 (2015)
Chen, Y., Hu, J., Kasabov, N., Hou, Z.G., Cheng, L.: Neucuberehab: a pilot study for eeg classification in rehabilitation practice based on spiking neural networks. Neural Inf. Process. 8228, 70–77 (2013)
Espinosa-Ramos, J.I., Capecci, E., Kasabov, N.: A computational model of neuroreceptor dependent plasticity (NRDP) based on spiking neural networks. IEEE Trans. Cogn. Dev. Syst. 99, 1–1 (2017)
Fusi, S.: Spike-driven synaptic plasticity for learning correlated patterns of asynchronous activity. In: International Conference on Artificial Neural Networks, pp. 241–247. Springer, Heidelberg (2002)
Gerstner, W.: Plausible Neural Networks for Biological Modelling. Kluwer Academic Publishers, Dordrecht (2001). vol What’s different with spiking neurons?
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Approach. Wiley, New York (1949)
Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, Heidelberg (2007)
Kasabov, N.: Evolving spatio-temporal data machines based on the neucube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016)
Kasabov, N., Dhoble, K., Nuntalid, N., Indiveri, G.: Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw. 41, 188–201 (2013)
Kulkarni, S., Simon, S.P., Sundareswaran, K.: A spiking neural network (SNN) forecast engine for short-term electrical load forecasting. Appl. Soft Comput. 13(8), 3628–3635 (2013)
Laña, I., Del Ser, J., Vélez, M., Vlahogianni, E.I.: Road traffic forecasting: recent advances and new challenges. IEEE Intell. Transp. Syst. Mag. 10, 93–109 (2018a)
Laña, I., Olabarrieta, I.I., Del Ser, J., Vélez, M.: On the imputation of missing data for road traffic forecasting: new insights and novel techniques accepted pending to be published. Transp. Res. Part C: Emerg. Technol. 90, 18–33 (2018b)
Levin, M., Tsao, Y.: On forecasting freeway occupancies and volumes. Transp. Res. Rec. 773, 47–49 (1980)
Madrid Open Data Portal (2018). http://datos.madrid.es. Accessed 31 Jan 2018
Reid, D., Hussain, A.J., Tawfik, H.: Spiking neural networks for financial data prediction. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–10. IEEE (2013)
Song, M.K.D., Abbott, L.F.: Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)
Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C: Emerg. Technol. 11(2), 121–135 (2003)
Thorpe, S.J., Gautrais, J.: Rank order coding. In: Computational Neuroscience, pp. 113–118. Springer, Heidelberg (1998)
Tu, E., Kasabov, N., Yang, J.: Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1305–1317 (2017)
Van Arem, B., Kirby, H.R., Van Der Vlist, M.J.M., Whittaker, J.C.: Recent advances and applications in the field of short-term traffic forecasting. Int. J. Forecast. 13(1), 1–12 (1997)
Van Hinsbergen, C., Van Lint, J., Sanders, F.: Short term traffic prediction models. In: World Congress on Intelligent Transport Systems (2007)
Vlahogianni, E.I., Golias, J.C., Karlaftis, M.G.: Short-term traffic forecasting: overview of objectives and methods. Transp. Rev. 24(5), 533–557 (2004)
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)
Wu, Y., Chen, F., Lu, C., Yang, S.: Urban traffic flow prediction using a spatio-temporal random effects model. J. Intell. Transp. Syst. Technol. Plan. Oper. 2450, 1–12 (2015)
Wysoski, S.G., Benuskova, L., Kasabov, N.: Evolving spiking neural networks for audiovisual information processing. Neural Netw. 23(7), 819–835 (2010)
Xu, J., Deng, D., Demiryurek, U., Shahabi, C., Van Der Schaar, M.: Mining the situation: spatio-temporal traffic prediction with big data. IEEE J. Sel. Top. Signal Process. 9(4), 702–715 (2015)
Yang, L., Zhongjian, T.: Prediction of grain yield based on spiking neural networks model. In: 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 171–174. IEEE (2011)
Zhu, Z., Peng, B., Xiong, C., Zhang, L.: Short-term traffic flow prediction with linear conditional Gaussian Bayesian network. J. Adv. Transp. 1–13 (2016)
Acknowledgements
This work was supported by the EU project Pacific Atlantic Network for Technical Higher Education and Research - PANTHER (grant number 2013-5659/004-001 EMA2), and by the Basque Government (EMAITEK program).
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Laña, I., Capecci, E., Del Ser, J., Lobo, J.L., Kasabov, N. (2018). Road Traffic Forecasting Using NeuCube and Dynamic Evolving Spiking Neural Networks. In: Del Ser, J., Osaba, E., Bilbao, M., Sanchez-Medina, J., Vecchio, M., Yang, XS. (eds) Intelligent Distributed Computing XII. IDC 2018. Studies in Computational Intelligence, vol 798. Springer, Cham. https://doi.org/10.1007/978-3-319-99626-4_17
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