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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Prediction of Traffic Intensity for Dynamic Street Lighting

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DOI: http://dx.doi.org/10.15439/2017F389

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 11491155 ()

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Abstract. In this paper, the problem of short-term prediction of traffic flow in a city traffic network is considered. This prediction is performed in order to provide input data to a dynamic control system for street lighting. The forecasting is done by a multi-layer using artificial neural network. Because of the limited number of sensors, the data is insufficient to describe the relation between the traffic intensity at a given point and the points in which the flow intensity is measured. The proposed approach is tested by using data from the centre of Krak\'ow. The prediction error turned to be low.


  1. A. Sędziwy and L. Kotulski, “A new approach to power consumption reduction of street lighting,” in 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), May 2015, pp. 1–5.
  2. A. S ̨edziwy, “A new approach to street lighting design,” LEUKOS, vol. 12, no. 3, pp. 151–162, 2016. http://dx.doi.org/10.1080/15502724.2015.1080122. [Online]. Available: http://dx.doi.org/10.1080/15502724.2015.1080122
  3. “Smart cities: background paper,” UK Department for Business, Innovation & Skills, Tech. Rep., 2013.
  4. A. Bahga and V. Madisetti, Internet of Things: A Hands-On Approach. Vijay Madisetti, 2014. ISBN 9780996025522. [Online]. Available: https://books.google.pl/books?id=mYmzoQEACAAJ
  5. “Light’s labour’s lost,” International Energy Agency, Tech. Rep., 2006.
  6. CEN, “CEN/TR 13201-1:2004, Road lighting. Selection of lighting classes,” European Commitee for Standardization, Brussels, Tech. Rep., 2004.
  7. Ernst, Sebastian, “Optimization of renewable energy-based autonomous device operation using simulation,” E3S Web Conf., vol. 10, p. 00020, 2016. http://dx.doi.org/10.1051/e3sconf/20161000020. [Online]. Available: https://doi.org/10.1051/e3sconf/20161000020
  8. A. Bielecki, M. Bielecka, and S. Ernst, “Proposal of an intelligent, predictive fuzzy controller for off-grid devices,” IFAC-PapersOnLine, vol. 49, no. 25, pp. 523 – 528, 2016. http://dx.doi.org/http://dx.doi.org/10.1016/j.ifacol.2016.12.077. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S240589631632715X
  9. F. Mwasilu, J. J. Justo, E.-K. Kim, T. D. Do, and J.-W. Jung, “Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration,” Renewable and Sustainable Energy Reviews, vol. 34, pp. 501 – 516, 2014. http://dx.doi.org/https://doi.org/10.1016/j.rser.2014.03.031. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1364032114001920
  10. I. Wojnicki, S. Ernst, and L. Kotulski, “Economic impact of intelligent dynamic control in urban outdoor lighting,” Energies, vol. 9, no. 5, p. 314, 2016. http://dx.doi.org/10.3390/en9050314. [Online]. Available: http://www.mdpi.com/1996-1073/9/5/314
  11. L. Guo, M. Eloholma, and L. Halonen, “Intelligent road lighting control systems,” Helsinki University of Technology, Department of Electronics, Lighting Unit, Tech. Rep., 2008. [Online]. Available: http://lib.tkk.fi/Diss/2008/isbn9789512296200/article2.pdf
  12. S. Fan, C. Yang, and Z. Wang, “Automatic Control System for Highway Tunnel Lighting,” in Computer and Computing Technologies in Agriculture IV, ser. IFIP Advances in Information and Communication Technology, D. Li, Y. Liu, and Y. Chen, Eds. Springer Boston, 2011, vol. 347, pp. 116–123. ISBN 978-3-642-18368-3. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-18369-0_14 http://www. springerlink.com/index/N1520PT884727374.pdf
  13. I. Wojnicki, S. Ernst, L. Kotulski, and A. Sędziwy, “Advanced street lighting control,” Expert Systems with Applications, vol. 41, no. 4, Part 1, pp. 999 – 1005, 2014. http://dx.doi.org/https://doi.org/10.1016/j.eswa.2013.07.044. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417413005319
  14. D. Leihs and A. Adamski, “Situational analysis in real-time traffic systems,” Procedia - Social and Behavioral Sciences, vol. 20, pp. 506 – 513, 2011. http://dx.doi.org/http://dx.doi.org/10.1016/j.sbspro.2011.08.057. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1877042811014376
  15. A. Adamski, “Intelligent traffic control in ITS systems,” Global Journal of Engineering Science and Research Management, vol. 2, no. 7, pp. 75–86, 2015.
  16. A. Abadi, T. Rajabioun, and P. A. Ioannou, “Traffic flow prediction for road transportation networks with limited traffic data,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 653–662, April 2015. http://dx.doi.org/10.1109/TITS.2014.2337238
  17. M. Castro-Neto, Y. Jeong, M. K. Jeong, and L. D. Han, “Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions.” Expert Syst. Appl., vol. 36, no. 3, pp. 6164– 6173, 2009. [Online]. Available: http://dblp.uni-trier.de/db/journals/eswa/eswa36.html#Castro-NetoJJH09a
  18. K. Y. Chan, T. S. Dillon, J. Singh, and E. Chang, “Neural-network-based models for short-term traffic flow forecasting using a hybrid exponential smoothing and levenberg #x2013;marquardt algorithm,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 644–654, June 2012. http://dx.doi.org/10.1109/TITS.2011.2174051
  19. J.-M. Chiou, “Dynamical functional prediction and classification, with application to traffic flow prediction,” Ann. Appl. Stat., vol. 6, no. 4, pp. 1588–1614, 12 2012. http://dx.doi.org/10.1214/12-AOAS595. [Online]. Available: http://dx.doi.org/10.1214/12-AOAS595
  20. W.-C. Hong, Y. Dong, F. Zheng, and C.-Y. Lai, “Forecasting urban traffic flow by svr with continuous aco,” Applied Mathematical Modelling, vol. 35, no. 3, pp. 1282–1291, 2011. http://dx.doi.org/10.1016/j.apm.2010.09.005
  21. Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, “Traffic flow prediction with big data: A deep learning approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, April 2015. http://dx.doi.org/10.1109/TITS.2014.2345663
  22. B. L. Smith and M. J. Demetsky, “Short-term traffic flow prediction models-a comparison of neural network and nonparametric regression approaches,” in Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2, Oct 1994. http://dx.doi.org/10.1109/IC-SMC.1994.400094 pp. 1706–1709 vol.2.
  23. B. L. Smith and M. J. Demetsky, “Short-term traffic flow prediction: neural network approach,” Transportation Research Record, vol. 1453, pp. 98–104, 1994.
  24. B. L. Smith and M. J. Demetsky, “Traffic flow forecasting: Comparison of modeling approaches,” Journal of Transportation Engineering, vol. 123, no. 4, pp. 261–266, Issue: object: http://dx.doi.org/10.1061/jtpedi.1997.123.issue-4, revision: rev:1479465310792-29747:http://dx.doi.org/10.1061/jtpedi.1997.123.issue-4, http://dx.doi.org/10.1061/(ASCE)0733-947X(1997)123:4(261)
  25. A. Stathopoulos and M. Karlaftis, “A multivariate state space approach for urban traffic flow modeling and prediction,” Transportation Research Part C, vol. 11, no. 2, pp. 121–135, April 2003. http://dx.doi.org/10.1016/S0968-090X(03)00004-4
  26. S. Sun and X. Xu, “Variational inference for infinite mixtures of gaussian processes with applications to traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 466–475, June 2011. http://dx.doi.org/10.1109/TITS.2010.2093575
  27. E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 3, pp. 211 – 234, 2005. http://dx.doi.org/https://doi.org/10.1016/j.trc.2005.04.007. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X05000276
  28. H. Yin, S. Wong, J. Xu, and C. Wong, “Urban traffic flow prediction using a fuzzy-neural approach,” Transportation Research Part C: Emerging Technologies, vol. 10, no. 2, pp. 85 – 98, 2002. doi: https://doi.org/10.1016/S0968-090X(01)00004-3. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0968090X01000043