|Paper title:||A Traffic Prediction Algorithm for Street Lighting Control Efficiency|
|Published in:||Issue 2, (Vol. 7) / 2013Download|
|Author(s):||LAVRIC Alexandru, POPA Valentin|
|Abstract.||This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor) prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.|
|Keywords:||Street Lighting Control; Traffic Prediction Algorithm; Neural Network; Dimming|
1. Alexandru Lavric, Valentin Popa, Ilie Finis, Adrian M. Gaitan, Adrian I. Petrariu, “Packet Error Rate Analysis of IEEE 802.15.4 under 802.11g and Bluetooth Interferences”, 9th International Conference on Communications, COMM 2012, pp. 259-262, 2012.
2. M. Young, Ç. Atici, T. Özçelebi, and J. J. Lukkien, “Exploring UserCentered Intelligent Road Lighting Design : A Road Map and Future Research Directions,” IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, May 2011.
3. Lavric Alexandru, Popa Valentin, Finis Ilie, Males Codrin, Gaitan, Adrian-Mihai, ”An original lighting monitoring and control system using Wireless Sensor Networks”, ECUMICT, pag. 167-173, 2012.
4. Alexandru LAVRIC, Valentin POPA, Ilie FINIS, Daniel SIMION,”The design and implementation of an energy efficient street lighting monitoring and control system,” Przeglad Elektrotechniczny, Nr. 11, pp. 312-316, 2012.
5. Alexandru LAVRIC, Valentin POPA, Ilie FINIS, Codrin MALES, Performance evaluation of Tree and Mesh ZigBee Network Topologies used in Street Lighting Control Systems, Przeglad Elektrotechniczny, pp. 168-171, 2013.
6. M. G. Shafer, E. Saputra, K. A. Bakar, and F. Ramadhani, “Modeling of Fuzzy Logic Control System for Controlling Homogeneity of Light Intensity from Light Emitting Diode,” 2012 ational Conference on Intelligent Systems Modelling and Simulation, pp. 71–75, Feb. 2012.
7. Alexandru Lavric, Valentin Popa, Codrin Males, Ilie Finis,” A Performance Study of ZigBee Wireless Sensors Network Topologies for Street Lighting Control Systems,” International Workshop on Mobile Ad-Hoc Wireless Networks, France, pp. 130-133, 2012. 8. M. C. Walden, T. Jackson, and W. H. Gibson, “Development of an Empirical Path-Loss Model for Street-Light Telemetry at 868 and 915 MHz,” Antennas and Propagatio, pp. 3389–3392, 2011.
9. Alexandru Lavric, Valentin Popa, Ilie Finis, “The Design of a Street Lighting Monitoring and Control System,” Conference Electrical and Power Engineering EPE , pp. 314-317, 2012. 10. Alexandru Lavric, Valentin Popa, Codrin Males, Ilie Finis, „New Technologies in Street Lighting”, International Word Energy System Conference (WESC), pp. 811-816, 2012.
11. Alexandru Lavric, Valentin Popa, „The Design and Development of a Street Lighting Monitoring and Control System”,DAS Doctoral Symposium, Suceava,pp. 66, 2012.
12. F. Leccese, “Remote-Control System of High Efficiency and Intelligent Street Lighting Using a ZigBee Network of Devices and Sensors,” IEEE Transactions on Power Delivery, vol. 28, no. 1, pp. 21–28, Jan. 2013.
13. Lavric A., V. Popa,”Comparative analysis of communication protocols and routing algorithms used in street lighting control systems,” Revista Sisteme Distribuite, pag.32-35, Suceava, 2011.
14. W. Z. Mai, “Case Study of A Highly-Reliable Dimmable Road Lighting System with Intelligent Remote Control Brief Review of Dimming Technology for Road Lighting Systems,” Power Electronics and Applications, pp. 1–10, 2005.
15. F. Leccese and Z. Leonowicz, “Intelligent wireless street lighting system,” 2012 11th International Conference on Environment and Electrical Engineering, pp. 958–961, May 2012.
16. Chen, Bor-Sen Sen, Peng, Sen-Chueh C., Wang, Ku-Chen,”Traffic modeling, prediction, and congestion control for high-speed networks: a fuzzy AR approach”, IEEE Transactions on Fuzzy Systems, pp. 491- 508, 2000.
17. De Fabritiis, Corrado, Ragona, Roberto, Valenti, Gaetano, ”Traffic Estimation And Prediction Based On Real Time Floating Car Data,” Conference on Intelligent Transportation Systems, pp. 197-203, 2008.
18. Furtlehner, Cyril, J-M. Lasgouttes, and Arnaud de La Fortelle. "A belief propagation approach to traffic prediction using probe vehicles." Intelligent Transportation Systems Conference,pp. 1022-1027, 2007.
19. Cheu, Ruey-Long. "Freeway traffic prediction using neural networks." Fifth International Conference on Applications of Advanced Technologies in Transportation Engineering. 1998. 20. Yuan Wen et al., “Traffic Incident Duration Prediction Based on KNearest Neighbor”, Applied Mechanics and Materials, pp. 253-255, 2012.
21. Xiangyu Zhou, Wenjun Wang, Long Yu “Traffic Flow Analysis and Prediction Based on GPS Data of Floating Cars”, Proceedings of the 2012 International Conference on Information Technology and Software Engineering Lecture Notes in Electrical Engineering Volume 210, pp 497-508, 2013.
22. Baumann, Martin, “A Neural Network Model for Driver’s LaneChanging Trajectory Prediction in Urban Traffic Flow,” Mathematical Problems in Engineering,2013.
23. Xu, H., Ying, J., Wu, H., Lin, F., “Public Bicycle Traffic Flow Prediction based on a Hybrid Model,” Appl. Math, 7(2), pp. 667-674, 2013.
24. XU Gang, JIN Hai He, LIU Jing, “Traffic Status Prediction and Analysis Based on Mining Frequent Subgraph Patterns,” Advanced Materials Research, pp. 2543-2548, 2013. 25. ”User Guide Rapid Miner” http://rapid-i.com/
26. „Trafic Measurements” http://www.aot.state.vt.us/planning/ documents/trafresearch/publications/pub.htm
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