MATHEMATICAL MODELING OF ROAD TRAFFIC FLOW IN URBAN AREAS WITH NEURAL NETWORK

Abstract

study of traffic flow in urban areas is of paramount importance due to its significant impact on transportation efficiency, environmental sustainability, and overall quality of life. Mathematical modeling serves as a powerful tool to analyze and understand the intricate behaviors exhibited by vehicular traffic within urban settings. This study explores the development and application of mathematical models to characterize the complex interactions among vehicles, pedestrians, infrastructure, and environmental factors in urban traffic systems. The study uses ANN based model for detecting the traffic flow in the urban. And the model is trained on dataset from Kaggle for the ANN algorithms with the mathematical liner equations. Additionally, the study delves into the incorporation of factors such as traffic signals, road geometries, driver psychology, and emerging technologies like connected and autonomous vehicles into these models. Through the synthesis of empirical data, advanced simulation techniques, and theoretical analysis, mathematical models offer valuable insights into traffic management strategies, congestion mitigation, urban planning, and the development of intelligent transportation systems for future cities