ItemMachine learning based car data prediction for network simulation using PETSC library(Wichita State University, 2023-12) Bogireddy, Sivaprasad Reddy; Asaduzzaman, AbuIn city areas, the profit of a business entity (i.e., shop) depends on the vehicular traffic at the nearest intersections. Argonne National Laboratory (ANL) and UChicago Argonne have developed Portable Extensible Toolkit for Scientific Computation (PETSc) and Data Management Network (DMNetwork) libraries to conduct network simulation. However, PETSc and DMNetwork do not provide a way to obtain and/or predict the number of vehicles to a network node. In this work, we develop a methodology to obtain real-time traffic data and predict future car data of the network nodes for profit analysis through network simulation. Open-Source Computer Vision Library (OpenCV) with the Background Subtractor algorithm and OpenCV with pre-trained You Only Look Once (YOLO) Version 3 datasets for vehicle detection are used for collecting car data using live video streaming file. The experimental results indicate that both methods yielded highly accurate traffic data, achieving a 90% accuracy rate, as cross-verified through manual counting for validation. We also integrate machine learning models, namely, Deep Neural Network (DNN) and Recurrent Neural Network (RNN), to forecast future car data for effective decision making. The DNN and RNN models are evaluated using three diverse datasets: Dataset-1 (represents a typical day car traffic), Dataset-2 (represents a morning high car traffic, and Dataset-3 (represents an evening high car traffic). Simulation results show that for both DNN and RNN models, Dataset-1 outperforms Dataset-2 and Dataset-3, achieving an R-squared value of 0.8 (indicating a high accuracy). Future work includes developing parallel tools for real-time car data retrieval, parallelizing file reading into PETSc DMNetwork, and incorporating additional features such as holidays, festivals, weekdays/weekends, special occasions, and weather forecasts for more robust future predictions.