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

especially in the contextof Sri Lanka. The results of both the sensor accuracy tests and the results obtained from the prediction models emphasize thatthe aforementioned objectives have been successfully achieved and the strategies that were utilized in this project toaddress the issues have been able to effectively contribute to the successful completion of the entire project by accomplishing their individual tasks. Here the efficiency of the methodology has been successfully illustrated by the practical implementation and using the obtained accuracy testresults. Further, there can be seen several benefits in this project. The traffic monitoring and forecasting system, which is introduced in this project, is low-cost as well as it can be easily implemented. This system can be implemented in any country, under any circumstances, since it has the ability to adapt to any type of traffic behavior. It is a well-known fact that monitoring vehicular movement is a really challenging task, especially in Sri Lanka, where the traffic behavior is messy and chaotic since vehicles do not move consistently followinga lane, maintaining adequate distance one after another. However, the traffic monitoring method that has been utilizedin this project which employs TFmini plus LiDAR sensor to monitor the traffic, is able to provide 89.86% accurate readings, avoiding difficult circumstances, even under an unsystematic traffic condition like Sri Lanka. Therefore, since this sensor system is able to address the traffic issue in Sri Lanka successfully, it proves the adaptability of implementingit anywhere under any type of traffic behavior. Moreover, even though image processing- based video traffic monitoring systems are commonly used in many other countries, it appears that those image processing-based systems require advanced technological feasibility as well asconsiderable capital to implement them. Therefore, when considering the existing technical feasibilities and the traffic infrastructures in Sri Lanka, this system is more appropriate as a low-cost as well as a system that can be implemented withlow technical capabilities. Especially its resistance to dust andrain makes it possible to place it anywhere in a country like Sri Lanka. The other thing is most of the traffic forecasting models need a sizable traffic database to train their prediction models. Prediction models based on image processing need a previous database to identify the vehicles by drawing the contours. And it takes significant time to train the model. But especially in the countries like Sri Lanka, there is no traffic databasegathered over a considerable time period. Therefore, the prediction model which is introduced in this project is more suitable for such countries because it does not require a large volume of data to be trained. Obtained prediction accuracy results depict that this prediction model can produce predictions with an accuracy rate of 74.20% even if it is trained using the data gathered over one week. Therefore, thisimplies that the absence of a proper traffic database would no longer be an issue for implementing traffic prediction modelswith this system. Therefore, it is clear that this traffic monitoring and forecasting system is a more practical approach to solving the traffic congestion issue effectively, especially in Sri Lanka, where there is no valid database regarding the traffic behaviors and congested areas, where the traffic behavior is messy and chaotic and where there are less advanced technical feasibilities. And the other important fact is that the accuracy testing results of the prediction models reveal that when the volume of the input data is increased it significantly increases the accuracy of the prediction model. Therefore, at the beginner level, this traffic monitoring and forecasting systemcan be implemented with the data gathered over a short period like one month, and later this can be developed further, with better forecasting accuracies by including data collected by training the system. R eferences R éférences R eferencias 1. T. Litman, “Congestion Costing Critique Victoria Transport Policy Institute 2”, [Online]. Available: www.vtpi.orgInfo@vtpi.org. 2. National Highway Traffic Safety Administration and others, “Traffic safety facts, 2012 data: pedestrians,” vol. 65, pp. 1–452, 2015. 3. “TTI’s 2012 Urban Mobility Report: Powered by INRIX Traffic Data Google Search.” https://www. google.com/search?q=TTI%27s+2012+Urban+Mo bi lity+Report%3A+Powered+by+INRIX+Traffic +Data&oq=TTI%27s+2012+Urban+Mobility+Rep ort%3A+Powered+by+INRIX+Traffic+Data&aqs= chrome..69i57.551j0j15&sourceid=chrome&ie=UT F-8 (accessed Apr. 28, 2022). 4. “Traffic surveillance by wireless sensor networks : Final report | CiNii Research all 検 索 .” https://cir. ii.ac.jp/all?q=Traffic%20surveillance%20by%20wirel ess%20sensor%20networks%20:%20Final%20repor t (accessed Apr. 28, 2022). 5. “INRIX 2021 Global Traffic Scorecard: As lockdowns ease UK city centres show signs of return to 2019 levels of congestion - INRIX.” https://inrix.com/ press-releases/2021-traffic-scorecard-uk/ (accessed Apr. 26, 2022). 6. K. Nellore and G. P. Hancke, “A Survey on Urban Traffic Management System Using Wireless Sensor Networks,” Sensors 2016, Vol. 16, Page 157 , vol. 16, no. 2, p. 157, Jan. 2016, doi: 10.3390/S16020157. 7. “Registered Motor Vehicles | Lanka Statistics.” htt ps:/ /lankastatistics.com/social/registered-motor-vehi cles.html (accessed Mar. 31, 2022). 8. L. N. v Alwis and N. Amarasingha, “Estimating the fuel loss during idling ofvehicles at signalized intersections in colombo,” in 2017 6th National Conference on Technology and Management © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 50 ( ) Year 2023 D Traffic Flow Forecast based on Vehicle Count