Enterprise-grade AI-powered platform that reduces traffic congestion by 40%, cuts emissions by 30%, and saves cities $2M+ annually through intelligent traffic optimization.
Our comprehensive dashboard provides instant insights into traffic patterns, vehicle detection, incident management, and predictive analytics - all updated in real-time.
Comprehensive suite of AI-powered tools designed for modern traffic operations, smart city infrastructure, and sustainable urban mobility.
Advanced YOLO v8 computer vision models with 96.7% accuracy for real-time vehicle detection, classification, and tracking across multiple camera feeds.
Machine learning models that forecast traffic patterns with 95.2% accuracy, enabling proactive traffic management and congestion prevention.
Automated incident detection and response system that identifies accidents, breakdowns, and unusual traffic patterns within 30 seconds.
Beautiful Android application built with Jetpack Compose, featuring Material Design 3 principles and real-time data synchronization.
Scalable microservices deployment on Google Cloud Platform with enterprise-grade reliability, security, and global edge distribution.
Comprehensive REST APIs, GraphQL endpoints, and WebSocket connections for seamless integration with existing city infrastructure and third-party systems.
Real-world performance data from production deployments across 200+ cities, managing over 50 million daily vehicle interactions.
See how TrafficFlow AI has transformed traffic management in cities worldwide, delivering measurable improvements in traffic flow, safety, and environmental impact.
Reduced downtown congestion by 45% during peak hours while improving emergency response times by 35%. Annual savings of $2.8M in traffic management costs.
Optimized traffic light timing across 500+ intersections, reducing average journey times by 22% and cutting emissions by 18% in Central London.
Implemented island-wide AI traffic management covering 1,200+ cameras and 800+ intersections with 99.8% system reliability and real-time incident detection.
Modern cloud-native microservices architecture designed for enterprise deployments, handling millions of daily transactions with 99.99% uptime.
Native Android application with modern UI/UX
High-performance Kotlin-based API server
Machine learning and computer vision pipeline
Optimized data storage and retrieval systems
Enterprise-grade cloud infrastructure
Carefully selected modern technologies chosen for performance, scalability, security, and developer experience across the entire stack.
Scalable pricing options designed to grow with your traffic management needs, from pilot programs to city-wide deployments.
Perfect for small-scale testing and proof of concept deployments
Comprehensive solution for medium to large city implementations
Large-scale deployments with custom requirements and dedicated support
All plans include free setup, training, and 30-day money-back guarantee
Comprehensive REST APIs, GraphQL endpoints, and real-time WebSocket connections designed for seamless integration with your existing infrastructure.
# Traffic Analytics
GET /api/v1/analytics/current
GET /api/v1/analytics/historical?period=24h&interval=1h
GET /api/v1/analytics/predictions?horizon=4h
# Vehicle Detection & Tracking
GET /api/v1/vehicles/count?intersection_id=123
GET /api/v1/vehicles/types?timeframe=1h
POST /api/v1/vehicles/detect
GET /api/v1/vehicles/tracking/{vehicle_id}
# Traffic Flow Management
GET /api/v1/traffic/flow?road_segment=main_st
GET /api/v1/traffic/congestion/live
POST /api/v1/traffic/signals/optimize
# Incident Management
GET /api/v1/incidents/active
POST /api/v1/incidents/report
PUT /api/v1/incidents/{id}/resolve
# Real-time Data Streams
WebSocket /ws/live-feed
WebSocket /ws/alerts
WebSocket /ws/traffic-updates{
"event": "traffic_update",
"timestamp": "2025-06-29T06:36:00Z",
"data": {
"intersection_id": "int_001",
"vehicle_count": 47,
"average_speed": 42.3,
"congestion_level": 0.23,
"signal_timing": {
"north_south": 45,
"east_west": 30
},
"detected_vehicles": [
{
"id": "v_12345",
"type": "car",
"speed": 38.5,
"direction": "north",
"confidence": 0.97
}
],
"predictions": {
"next_15min": "light_traffic",
"confidence": 0.94
}
}
}// Initialize TrafficFlow API Client
val client = TrafficFlowClient.Builder()
.baseUrl("https://api.trafficflow.ai/v1/")
.apiKey("your_api_key_here")
.enableWebSocket()
.enableRetry(maxRetries = 3)
.build()
// Get real-time traffic analytics
lifecycleScope.launch {
try {
val analytics = client.analytics.getCurrentTraffic()
updateUI(analytics)
} catch (e: ApiException) {
handleError(e)
}
}
// Subscribe to live traffic updates
client.websocket.subscribe("traffic_update") { update ->
runOnUiThread {
updateDashboard(update.data)
}
}
// Detect vehicles in camera feed
val detection = client.vehicles.detect(
image = cameraFrame,
confidence = 0.8f,
includeTracking = true
)
# Install TrafficFlow Python SDK
# pip install trafficflow-python-sdk
from trafficflow import TrafficFlowClient
import asyncio
# Initialize client
client = TrafficFlowClient(
api_key="your_api_key_here",
base_url="https://api.trafficflow.ai/v1/"
)
# Get current traffic analytics
async def get_traffic_data():
analytics = await client.analytics.get_current()
return analytics
# Real-time WebSocket connection
async def listen_to_updates():
async with client.websocket.connect() as ws:
async for message in ws.listen("traffic_update"):
print(f"Traffic update: {message}")
# Vehicle detection from video feed
detection_result = client.vehicles.detect_from_image(
image_path="camera_feed.jpg",
return_annotations=True
)
print(f"Detected {len(detection_result.vehicles)} vehicles")
Complete API reference with interactive examples, SDKs for 8+ languages, and detailed integration guides.
Sub-50ms response times globally with 99.99% uptime SLA and automatic scaling for traffic spikes.
Bank-grade security with OAuth 2.0, API key management, and comprehensive audit logging.
Hear from transportation professionals who have transformed their cities with TrafficFlow AI.
"TrafficFlow AI reduced our downtown congestion by 45% in just 6 months. The ROI was immediate, and our citizens notice the difference every day. The platform is incredibly intuitive and the support team is world-class."
"The predictive analytics are game-changing. We can now anticipate traffic issues before they happen and route emergency vehicles 35% faster. This technology is saving lives and improving our emergency response capabilities."
"Implementing TrafficFlow AI across our entire island was seamless. The 99.8% uptime and real-time incident detection have exceeded our expectations. Our traffic management has never been more efficient."
"The environmental impact has been remarkable. We've cut vehicle emissions by 18% in Central London while improving journey times. TrafficFlow AI is helping us build a more sustainable city."
"The integration with our existing systems was flawless. The APIs are well-documented, and the development team provided exceptional support throughout our deployment. Highly recommended."
"TrafficFlow AI has transformed how we manage traffic in Toronto. The $2.8M annual savings in operational costs alone justified the investment, and the improved citizen satisfaction is priceless."
Comprehensive deployment guides, automated setup scripts, and expert support to get your traffic management system running quickly.
# Clone the repository
git clone https://github.com/Alims-Repo/Smart-Traffic-Management-System.git
cd Smart-Traffic-Management-System/android-app
# Configure environment
cp config/app.properties.example config/app.properties
# Edit config/app.properties with your API credentials
# Build and install
./gradlew assembleRelease
./gradlew installRelease
# Or download from Google Play Store
# Search: "TrafficFlow AI" by Alims-Repo# Navigate to backend directory
cd backend-api
# Configure environment variables
cp .env.example .env
# Edit .env with database and API configurations
# Using Docker (Recommended)
docker-compose up -d
# Or build from source
./gradlew build
java -jar build/libs/trafficflow-api-1.0.jar
# Health check
curl http://localhost:8080/health# Navigate to AI service directory
cd ai-detection-service
# Create virtual environment
python3.11 -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Download pre-trained models
python scripts/download_models.py --model yolov8x
# Configure GPU support (optional)
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
# Start the AI service
python main.py --port 8001 --workers 4# Authenticate with Google Cloud
gcloud auth login
gcloud config set project your-project-id
# Deploy using our Terraform scripts
cd infrastructure/terraform
terraform init
terraform plan -var="project_id=your-project-id"
terraform apply
# Or use one-click deployment script
./scripts/deploy-gcp.sh --project your-project-id \
--region us-central1 \
--zone us-central1-a
# Monitor deployment
kubectl get pods -n trafficflowJoin 200+ cities worldwide in implementing intelligent traffic solutions. Our team of experts is ready to help you get started with a custom implementation plan.
45-minute strategy session
Schedule a free consultation with our traffic management experts to discuss your specific needs and challenges.
Book Consultation30-day proof of concept
Start with a small-scale pilot to demonstrate ROI before full deployment. Includes setup, training, and support.
Start PilotSee the platform in action
Watch a personalized demo of TrafficFlow AI with real traffic data from a live deployment.
Watch DemoFor large deployments
📧 enterprise@trafficflow.ai
📞 +1 (555) 123-4567
🕒 Mon-Fri, 9AM-6PM EST
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📧 support@trafficflow.ai
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