Machine Learning-Based Urban Traffic Flow Prediction and Route Optimisation Using City ...

Objective: To design and deploy an AI-powered system that predicts urban traffic flow and optimises vehicle routing using real-time city sensor data for reduced congestion and travel times.

Urban traffic congestion in Indian cities like Mumbai, Bengaluru, and Delhi leads to significant delays, air pollution, and economic losses for commuters, logistics operators, and city authorities. The unpredictable nature of traffic jams negatively impacts emergency services, public transport efficiency, and overall city productivity.

The proposed solution is an end-to-end AI system that ingests live city sensor data (from CCTV, IoT traffic sensors, and GPS feeds), applies advanced machine learning models for short-term traffic flow prediction, and dynamically suggests optimal routes to users via a web/mobile dashboard. The team will use Indian open datasets (e.g., MCGM, Open Data India) and, where possible, simulate realistic sensor feeds.

Key deliverables include: (1) a data ingestion pipeline from real or simulated city sensors, (2) trained ML/DL models (e.g., LSTM, GNN) for traffic forecasting, (3) an interactive dashboard providing real-time route recommendations, (4) integration with mapping tools (OpenStreetMap API), and (5) live demonstration with test cases and quantitative performance validation.

This solution can be adopted by municipal authorities, taxi fleets, and logistics firms to improve transport efficiency, reduce carbon emissions, and enhance commuter experience. The system is scalable to multiple Indian cities and supports future integration with smart city initiatives.