Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{173749,
        author = {Priyankit Chatterjee and Aviral and Dr. Khyati Zalawadia},
        title = {TraffiFlow: A Data Driven Approach For Traffic Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1611-1617},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173749},
        abstract = {Urban traffic congestion is increasingly becoming an issue, resulting in mobility inefficiencies, economic losses, and environmental pollution. Conventional traffic prediction approaches frequently cannot cope with intricate, dynamic patterns. This paper introduces TraffiFlow, a data-driven solution based on machine learning to improve traffic forecasting and real- time management. Historical traffic data, such as timestamps and vehicle volumes, are preprocessed using time-series feature extraction, missing value management, and normalization. A Random Forest Regressor forecasts car traffic according to primary time-based characteristics, assessed in terms of MSE and R² measures. Findings illustrate robust short-term predictions, with further improvements},
        keywords = {},
        month = {March},
        }