Disha - Urban Traffic Intelligence

Datathon 2026

Problem Statement

Design a machine learning-based time-series forecasting system to predict traffic congestion levels in urban environments. The system should integrate data from multiple sources (GPS, traffic sensors, weather, city events) to generate accurate hourly or daily congestion forecasts. It must handle multivariate inputs, adapt to evolving traffic patterns, and produce reliable predictions that support real-time traffic control, route planning, and congestion mitigation strategies.

Prerequisites

• Python 3.10+
• Node.js 18+

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Setup

1. Backend

cd neuroflow_backend
python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt

2. Frontend

cd neuroflow_frontend
npm install

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Run

Start backend and frontend in separate terminals.

Terminal 1 — Backend:

cd neuroflow_backend
source .venv/bin/activate   # Windows: .venv\Scripts\activate
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Terminal 2 — Frontend:

cd neuroflow_frontend
npm run dev

Open http://localhost:5173.

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