README.md

metadata

title: AI Traffic Adaptation System
emoji: 🚦
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 6.30.0
app_file: app.py
pinned: false
license: mit

Adaptive Multi-Modal AI Traffic Signal Control & Safety System

An advanced Machine Learning and Deep Learning solution for real-time intelligent traffic management, safety hazard detection, near-miss risk calibration, emergency vehicle preemption, and spatio-temporal flow prediction.

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🚀 Live Demo & Production Architecture

This repository runs as a Gradio Hugging Face Space and can also be run locally or in Docker.

• Frontend: React 19 + Tailwind CSS + Lucide Icons (Production SPA in frontend/dist)
• Backend API: FastAPI (REST endpoints + real-time WebSocket telemetry at /ws/live)
• Machine Learning Pipelines:
  1. Spatio-Temporal GNN (GRU + Graph Convolutional Network over 36-detector network topology)
  2. Computer Vision Perception (YOLOv11 nano for traffic density + custom emergency vehicle detector)
  3. Near-Miss Risk Engine (Calibrated Random Forest & Gradient Boosted Decision Trees on kinematic conflict features: TTC, PET, DRAC)
  4. Accident Severity Classifier (Deep CNN with Platt/Isotonic calibration)
  5. Dynamic Traffic Signal Decision Engine (Predictive adaptive cycle allocation & priority preemption)

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🐳 Docker Deployment

1. Build Docker Image Locally

docker build -t ai-traffic-adaptation .

2. Run Container Locally

docker run -p 7860:7860 ai-traffic-adaptation

Open your browser at: http://localhost:7860

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☁️ Deploying to AWS

The included PowerShell script deploys the Docker image to Amazon ECR and creates a public Amazon ECS Express Mode service in ap-south-1. ECS Express Mode provisions the load balancer, health checks, networking, and scaling around the container. The service uses CPU-based Fargate tasks; video inference can be slower than on a GPU host.

Prerequisites: AWS CLI v2 with ECS Express Mode support, Docker Desktop with the Linux engine running, an AWS identity allowed to create ECR repositories, IAM roles and policies, and ECS Express services, plus a default VPC in the selected region.

aws login
.\deploy\aws\deploy-ecs-express.ps1 -Region ap-south-1

The script creates an ECR repository and the two ECS service roles if they are missing, pushes the image, then creates the service with /dashboard/api/status as its health check. AWS charges for the running ECS tasks, load balancer, and related resources. AWS App Runner is not used because AWS stopped accepting new App Runner customers on March 31, 2026; ECS Express Mode is AWS’s recommended path for new deployments.

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🤗 Deploying to Hugging Face Spaces

1. Create a new Space on Hugging Face Spaces.
2. Choose Gradio as the Space SDK. The Space is configured for the Gradio runtime.
3. Clone your Space repository or add it as a git remote:

   git remote add space https://huggingface.co/spaces/<YOUR_USERNAME>/<YOUR_SPACE_NAME>
   

4. Push the project code:

   git push space main
   

5. Hugging Face Spaces will launch app.py; the React dashboard and its FastAPI APIs are mounted under /dashboard.

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📊 Analytical Visualizations & Model Artifacts

• Calibration & Reliability Curves: reports/figures/accident/calibration_curve.png, reports/figures/near_miss/calibration_comparison.png
• ROC-AUC & Precision-Recall Curves: reports/figures/near_miss/roc_pr_curve_random_forest_A.png, reports/figures/near_miss/calibrated_precision_recall.png
• Confusion Matrix: reports/figures/near_miss/confusion_matrix_random_forest_A_test.png
• Feature Importance: reports/figures/near_miss/feature_importance_random_forest_A.png
• Controller Benchmarking: reports/near_miss/model_evaluation.md, results/evaluation/run_20260906_004744/raw_results.csv

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📈 Benchmark Comparison

Metric	Fixed-Time Baseline	Actuated (Density)	Predictive Adaptive (GNN)	Integrated AI (Ours)	Improvement
Max Waiting Time (s)	26.0 s	1.0 s	13.0 s	3.0 s	88.5% reduction
Avg Queue Length (veh)	6.95	0.019	0.569	0.077	98.9% reduction
Total Vehicle Stops	19 stops	1 stop	6 stops	2 stops	89.5% reduction
Near-Miss Detection F1	0.00 (Dummy)	0.681 (LR)	0.729 (GBDT)	0.755 (Calibrated RF)	+75.5 pts
Near-Miss ROC-AUC	0.500	0.912	0.916	0.929	+42.9 pts
# ATA_deploy