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

© 2023. Pavanee Weebadu Liyanage & K. P. G. C. D. Sucharitharathna. This research/review article is distributed under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BYNCND 4.0). You must give appropriate credit to authors and reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https://creativecommons.org/ licenses/by-nc-nd/4.0/. Global Journal of Computer Science and Technology : D Neural & Artificial Intelligence Volume 23 Issue 2 Version 1.0 Year 2023 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Traffic Flow Forecast based on Vehicle Count By Pavanee Weebadu Liyanage & K. P. G. C. D. Sucharitharathna Curtin University Perth Abstract- Real-time traffic predictions have now become a time-being need for efficient traffic management due to the exponentially increasing traffic congestion. In this paper, a more pragmatic traffic management system is introduced to address traffic congestion, especially in countries such as Sri Lanka where there is no proper traffic monitoring database. Here the real- time traffic monitoring is performed using TFmini Plus light detection and ranging (LiDAR) sensor and vehicle count for next five minutes will be predicted by feeding consecutively collected data into the LSTM neural network. More than ten separate prediction models were trained, varying both window size and the volume of input data delivered to train the models. Since the accuracy results of all prediction models were above 70%, it demonstrates that this system can produce accurate predictions even if it is trained using less input data collection. Similarly the sensor accuracy test also resulted in 89.7% accuracy. Keywords: traffic monitoring, lstm neural network traffic predictions, vehicle count, traffic flow forecast, real- time traffic monitoring, lidar sensor traffic monitoring. GJCST-D Classification: LCC: TE175-178 TrafficFlowForecastbasedonVehicleCount Strictly as per the compliance and regulations of: