Optimizing Urban Mobility: AI-Powered Smart Traffic Management System

Project Summary

The Smart Traffic Management System is an integrated platform designed for smart city infrastructure, utilizing IoT sensors deployed on roads and intersections to collect real-time traffic data. Powered by AI algorithms for predictive traffic flow analysis and anomaly detection, the system includes a cross-platform mobile app for commuters to receive personalized route recommendations and alerts. Built with Python for AI processing, Flutter for mobile development, and hosted on AWS with Docker and Kubernetes for orchestration, the solution enables city authorities to optimize traffic signals, reduce delays, and promote sustainable urban mobility.

Challenges & Solutions

Challenges

Integrating real-time data from distributed IoT devices in dynamic urban environments.
Achieving accurate AI predictions for traffic patterns amid variable conditions like weather or events.
Providing a seamless, scalable mobile experience for diverse users across iOS and Android.

Solutions

Deployed robust IoT sensors with edge computing capabilities to handle data collection and initial processing, ensuring low-latency transmission to the central AI system.
Developed advanced AI models using computer vision and NLP for analyzing camera feeds and social media data, enhanced with LLM for natural language queries in the mobile app.
Utilized Flutter for cross-platform development, enabling real-time updates and personalized features while maintaining performance and usability.

Technology Stack

AI & Data Processing: Python, NLP, LLM, Computer Vision
Mobile: Flutter, iOS, Android
Cloud & Deployment: AWS, Docker, Kubernetes
APIs: RESTful APIs
Tools: Visual Studio Code, Git

Client Benefits

Reduced Congestion: AI predictions optimized traffic flow, reducing average commute times by 25% in pilot deployments.
Enhanced Safety: Real-time alerts via the mobile app improved driver awareness, decreasing accident rates.
Scalable Infrastructure: Docker and Kubernetes enabled seamless expansion to additional city zones, supporting growing urban demands.

Approach

IoT Data Acquisition

Installed IoT sensors and gateways to gather real-time traffic data, integrated with AWS for secure, scalable storage and processing.
AI-Driven Analytics

Trained machine learning models with Python and PyTorch for predictive analytics, incorporating computer vision for vehicle detection and LLM for intelligent query handling.
Mobile App Development

Built a Flutter-based app for iOS and Android, offering features like live traffic maps, route optimization, and AI-powered alerts, deployed with Docker and Kubernetes for efficient scaling.

Talk to us