International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Traffic Flow Prediction |
|---|---|
| ABSTRACT | Traffic flow prediction plays a critical role in intelligent transportation systems by enabling efficient traffic management, reducing congestion, and improving road safety. This paper presents a machine learning-based framework for traffic flow prediction using Python and interactive visualization through Power BI. Historical traffic datasets are preprocessed using Python libraries, including Pandas and NumPy, followed by feature engineering and data normalization to improve prediction performance. Multiple machine learning algorithms are evaluated, and the most accurate model is selected based on performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The predicted traffic flow is integrated with Power BI to develop interactive dashboards that visualize traffic density, peak-hour patterns, daily and monthly trends, and route-specific insights. |
| AUTHOR | DR. T.V.S SRIRAM, S. JAYAPRADHA, D. BALAJI Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060102 |
| pdf/102_Traffic Flow Prediction.pdf | |
| KEYWORDS | |
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