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@article{200213,
        author = {Kalpana Sonval and Arya Jagtap and Yash Divekar and Mahesh Deshmukh},
        title = {Traffic Congestion Prediction and Alert System Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {496-499},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200213},
        abstract = {Traffic congestion is one of the most critical challenges in urban transportation management. With rapid urbanization and growing vehicle density, cities face severe road congestion that leads to delays, fuel wastage, increased pollution, and economic losses. This study proposes an intelligent Traffic Congestion Prediction and Alert System (Flow Sense) that leverages machine learning techniques to analyze real-time traffic data and predict congestion levels proactively. The system collects traffic data from multiple sources including vehicle count sensors, GPS feeds, and road cameras. Data preprocessing techniques including noise removal, normalization, and feature extraction are applied. Exploratory Data Analysis (EDA) is performed to uncover patterns in time-of-day traffic, road type behavior, and congestion triggers. Machine learning algorithms such as Random Forest, Support Vector Machine (SVM), XGBoost, and Long Short-Term Memory (LSTM) networks are used to train predictive models. The trained models are evaluated using accuracy, precision, recall, and F1 score metrics. Results demonstrate that XGBoost achieves the highest accuracy of 92%, followed by LSTM at 90%. The system integrates a real-time alert mechanism to notify commuters and traffic authorities of upcoming congestion, enabling timely route adjustments and improved urban mobility.},
        keywords = {Traffic Congestion Prediction, Machine Learning, FlowSense, XGBoost, LSTM, Smart Transportation, Alert System, Urban Mobility.},
        month = {May},
        }