Traffic Flow Prediction Using Machine Learning: A Novel Framework

Paper ID: EIJTEM_2025_12_3_9-16

Author's Name: Dr. Kiruthika B, Mr. Sumeet Aanakod, Mr. Hardik Jadhav

Volume: 12

Issue: 3

Year: 2025

Page No: 9-16

Abstract:

Rapid urbanization has increased urban populations, posing challenges in transportation management, including safety risks, traffic congestion and pollution. Accurate traffic flow forecasting is essential to mitigating congestion. This paper proposes a two-level machine learning framework: a clustering model to analyze sensor and RTO data, followed by supervised models for precise traffic predictions. Integrating RTO data enhances accuracy by incorporating historical trends. While dependent on data availability, the framework extends to areas with limited sensor coverage. Validation in a real-world scenario highlights its potential for smarter traffic management and urban planning.

Keywords: traffic flow forecasting, machine learning, traffic congestion, urban scenario, smart city, transportation management, traffic department data, artificial intelligence.

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