Abstract: The Traffic Flow Prediction System using Artificial Intelligence represents a ground breaking solution to the escalating challenges posed by urban traffic congestion. Traditional traffic forecasting methods often struggle to accommodate the complexity of real- world traffic dynamics, necessitating a paradigm shift. In response, this project introduces an innovative framework that harnesses the capabilities of Artificial Intelligence (AI) to transform transportation management. By fusing machine learning and deep learning methodologies, the proposed system adeptly analyses an amalgamation of data sources. These encompass historical traffic patterns, meteorological conditions. These intricate data orchestration facilitates the system in generating accurate predictions about impending traffic conditions. The predictions, in turn, empower commuters with informed decision-making capabilities and enable traffic managers to implement proactive strategies for congestion mitigation. The system's holistic architecture encompasses multifaceted phases, including data aggregation, preprocessing, training AI models, real-time data assimilation, and the visual rendering of predictions. The amalgamation of these phases culminates in a sophisticated AI- powered tool capable of revolutionizing urban commuting By empowering stakeholders with timely insights and predictions, this system aspires to navigate the intricate web of urban traffic dynamics, fostering enhanced mobility and efficiency within city landscapes
Keywords: Urban traffic congestion, Traffic forecasting, Historical traffic patterns, Commuters
Works Cited:
Om Mohite, Pranit Jadhav, Sagar Gite, Sudhir B. Lande " A systematic review of Artificial Intelligence based Traffic Flow Prediction System ", IJIREEICE International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, vol. 11, no. 11, pp. 8-14, 2023. Crossref https://doi.org/: 10.17148/IJIREEICE.2023.111102
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