Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

© 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 51 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count (NCTM) , 2017, pp. 132–137. doi: 10.1109/ NCTM.2017.7872841. 9. A. S. Kumarage, “URBAN TRAFFIC CONGESTION: THEPROBLEM & SOLUTIONS Paper Published in the Economic Review, Sri Lanka,” Moratuwa, Sri Lanka, 2004. Accessed: Mar. 28, 2022. [Online]. Available: https://kumarage.files.wordpress.com/20 15 /03/2004-p-06-ut- urban-traffic-congestion-the- problems-and-solutions-economic- review-sri-lanka- 2004.pdf 10. “Kanishka Werawella Presents Solutions to Traffic Congestion inSri Lanka | Institute of policy studies Sri Lanka.” https:/ /www.ips.lk/kanishka-werawella- presents-solutions-to- traffic-congestion-in-sri-lanka/ (accessed Apr. 03, 2022). 11. “Traffic information system for Sri Lanka.” http://dl. lib.uom.lk/handle/123/10529 (accessed Mar. 31, 2022). 12. .“Hong Kong Intelligent Transport System (ITS) - Verdict Traffic.” https://www.roadtraffic- technology. com/projects/hong-kong/ (accessed May 03, 2022). 13. “A Distributed Instrument for Measuring Traffic in Short-Term Work Zones.” https://trid.trb.org/view/ 1229740 (accessed May 03,2022). 14. “TS-45A Multi dimensional Model for Vehicle Impact on Traffic Safety, Congestion, and Environment | Michigan-Ohio (MIOH) University Transportation Center (UTC).” https://mioh- utc.udmercy.edu/ research/ts-45/index.htm (accessed May 03, 2022). 15. W. Wen, “A dynamic and automatic traffic light control expert system for solving the road congestion problem,” Expert Systems with Applications: An International Journal , vol. 34, no. 4, pp. 2370–2381, May 2008, doi: 10.1016/J.ESWA. 2007.03.007. 16. M. Bernas, B. Płaczek, W. Korski, P. Loska, J. Smyła, and P. Szymała, “A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring,” Sensors 2018, Vol. 18, Page 3243 , vol. 18, no. 10, p. 3243, Sep. 2018, doi: 10.3390/ S18103243. 17. X. Mao, S. Tang, J. Wang, and X. Y. Li, “ILight: Device-free passive tracking using wireless sensor networks,” IEEE Sensors Journal , vol. 13, no. 10, pp. 3785–3792, 2013, doi: 10.1109/JSEN.2013.22679 59. 18. R. Gade and T. B. Moeslund, “Thermal cameras and applications: a survey,” Machine Vision and Applications 2013 25:1 , vol. 25, no. 1, pp. 245–262, Nov. 2013, doi: 10.1007/S00138-013-0570-5. 19. I. Gulati and R. Srinivasan, “Image processing in intelligent traffic management,” International Journal of Recent Technology and Engineering , vol. 8, no. 2 Special Issue 4, pp. 213–218, Jul. 2019, doi: 10.35940/IJRTE.B1040.0782S419. 20. M. R. Islam, N. I. Shahid, D. T. Ul Karim, A. al Mamun, and M. 21. K. Rhaman, “An efficient algorithm for detecting traffic congestion and a framework for smart traffic control system,” International Conference on Advanced Communication Technology, ICACT , vol. 2016-March, pp. 802–807, Mar. 2016, doi: 10.1109/ICACT.2016.7423566. 22. G. Padmavathi, D. Shanmugapriya, M. Kalaivani, G. Padmavathi, D. Shanmugapriya, and M. Kalaivani, “A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks,” Wireless Sensor Network , vol. 2, no. 2, pp. 173–185, Mar. 2010, doi: 10.4236/WSN.2010.22023. 23. D. Guilbert, C. le Bastard, S. S. Ieng, and Y. Wang, “State Machine for Detecting Vehicles by Magnetometer Sensors,” IEEE Sensors Journal , vol. 16, no. 13, pp. 5127–5128, Jul. 2016, doi: 10.1109/JSEN.2016.2560903. 24. M. Tomic, P. T. Sullivan, and V. K. Mcdonald, “Wireless, acoustically linked, undersea, magne tometer sensor network,” MTS/IEEE Biloxi - Marine Technology for Our Future: Global and Local Challenges, OCEANS 2009 , 2009, doi: 10.23919/ OCEANS.2009.5422465. 25. T. W. Yeh, S. Y. Lin, H. Y. Lin, S. W. Chan, C. T. Lin, and Y. Y. Lin, “Traffic Light Detection using Convolutional Neural Networks and Lidar Data,” Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 , Dec. 2019, doi: 10.1109 /ISPACS48206.2019.8986310. 26. B. Anand, V. Barsaiyan, M. Senapati, and P. Rajalakshmi, “Region of Interest and Car Detection using LiDAR data for Advanced Traffic Management System,” IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings , Jun. 2020, doi: 10.1109/WF-IOT48130.2020.9221354. 27. J. Wu, H. Xu, and J. Zheng, “Automatic background filtering and lane identification with roadside LiDAR data,” IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC , vol. 2018-March, pp. 1–6, Mar. 2018, doi: 10.1109/ITSC.2017.8317723. 28. J. Barros, M. Araujo, and R. J. F. Rossetti, “Short- term real-time traffic prediction methods: A survey,” in 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) , 2015, pp. 132–139. doi: 10.1109/MTITS. 2015.7223248. 29. S. M. H. Hosseini, B. Moshiri, A. Rahimi-Kian, and B. N. Araabi, “Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction,” Promet-traffic & Transportation , vol. 26, pp. 393–403, 2014. 30. D. Zeng, J. Xu, J. Gu, L. Liu, and G. Xu, “Short term traffic flow prediction using hybrid ARIMA and ANN models,” Proceedings 2008 Workshop on Power Electronics and Intelligent Transportation System,