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@article{193761,
        author = {Ms  .  B.Keerthana and Kota Keerthi and Shaik Mahaboob Basha and Kailash Dipanshu and Shaik  Nadimulla Javeed Basha},
        title = {Real-Time Traffic Forecasting and Optimization Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1374-1381},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193761},
        abstract = {The fast-growing rate of urbanization, and presence of more vehicles, has led to the need of smarter systems to control the traffic and congestion on the road. The traditional ways of managing traffic cannot manage the fast ever-changing and real time situations resulting in delays and inefficient vehicle movement. To overcome this critical problem, this system applies a real-time traffic prediction and optimization model that is based on sophisticated methods of machine learning. It gathers traffic data that is analyzed with predictive models, which anticipate possible traffic congestions and recommends the optimum paths. The use of such techniques as Random Forest, Support Vector machines, K-Nearest Neighbors, and XGBoost can be attributed to these techniques being efficient in predicting traffic trends and comprehending more complicated patterns of people movement within cities. The system is very accurate, achieving an accuracy of 89% in predicting traffic conditions which significantly increases the performance of the total traffic. The results indicate that machine learning could be one of the essential elements in the creation of flexible and dependable traffic systems, which could expand and could serve future needs and growth, to help develop smarter and sustainable urban spaces.},
        keywords = {Real-Time Traffic Prediction, Machine Learning, Predictive Modeling, Route Optimization, Traffic Congestion Reduction.},
        month = {March},
        }