PROOF OF CONCEPT

Edge Intelligence for Smart Transportation

Transform existing transportation infrastructure into an intelligent, predictive system using Raspberry Pi edge nodes and AI-powered pattern recognition

Intelligent Architecture Design

From reactive sensors to predictive intelligence

RWIS CCTV Pavement

Existing Infrastructure

RWIS, Traffic APIs, Weather Services, CCTV Systems

→
BCM 8GB SD PWR HDMI Raspberry Pi 5

Raspberry Pi 5

Edge ML Processing

• Data aggregation
• Pattern detection
• Token generation
• 95% data reduction
→
CentOS AI Server

Central AI Server

Docker on CentOS

• Deep pattern analysis
• Predictive modeling
• Intelligent routing
• Real-time decisions
→
WIND ADVISORY TRUCKS/RVS REDUCE SPEED

CMS Alert System

Adaptive Messaging

• Dynamic alerts
• Vehicle-specific warnings
• Predictive messaging
• Multi-condition logic

Real-World Use Case

High wind events and commercial vehicle safety

📍 I-80 Wyoming Wind Corridor

Critical transportation route affected by severe cross-winds, impacting commercial vehicle stability and safety.

1

Traditional Reactive System

Wind speed hits 45 mph threshold → Generic CMS alert activated → Same message for all vehicles

CMS: "HIGH WIND WARNING - REDUCE SPEED"
2

Edge Intelligence Enhancement

Raspberry Pi processes wind patterns, vehicle classifications, and weather forecasts in real-time

ANALYSIS: Wind gusts 47mph @ 285° + Class 8 trucks detected + Precipitation forecast → Elevated risk profile
3

Intelligent Response

AI-powered system generates vehicle-specific messaging and routing recommendations

CMS A: "TRUCKS/RVS - EXTREME CROSSWIND - CONSIDER ALTERNATE ROUTE"
CMS B: "PASSENGER VEHICLES - REDUCE SPEED 15 MPH"
4

Predictive Messaging

System anticipates conditions 30 minutes ahead based on weather models and traffic patterns

UPSTREAM CMS: "HIGH WINDS AHEAD - TRUCKS CONSIDER I-80 ALT AT LARAMIE"

Expected Performance Improvements

Quantified benefits of edge intelligence implementation

65%
Reduction in Weather-Related Incidents
40%
Improved Response Time
95%
Data Processing Efficiency
24/7
Autonomous Operation

Technical Components

Purpose-built edge intelligence stack

🧠

Edge ML Pipeline

TensorFlow Lite models optimized for Raspberry Pi hardware, processing sensor data with <2ms latency

📡

Multi-Protocol Gateway

Unified data ingestion from RWIS, traffic APIs, weather services, and CCTV systems via MQTT/REST/FTP

🔄

Pattern Recognition Engine

Real-time anomaly detection and trend analysis using sliding window algorithms and statistical modeling

📊

Predictive Analytics

30-minute forward prediction using ensemble methods combining weather models and traffic patterns

💾

Token-Based Data Compression

Intelligent data reduction achieving 95% bandwidth savings while preserving critical event information

🚨

Adaptive CMS Controller

Dynamic message generation with vehicle-specific logic and multi-condition rule processing

Traditional vs. Edge Intelligence

Comprehensive comparison of system capabilities

Capability Traditional RWIS Edge Intelligence Improvement
Response Time 5-15 minutes < 30 seconds ✓ 20x faster
Message Specificity Generic alerts Vehicle-specific ✓ Targeted messaging
Predictive Capability ✗ Reactive only ✓ 30-min forecast ✓ Proactive alerts
Data Processing Centralized Distributed edge ✓ 95% bandwidth reduction
Autonomous Operation Manual oversight required Fully autonomous ✓ 24/7 operation
Integration Complexity Single protocol Multi-protocol gateway ✓ Unified platform

Ready to Deploy Edge Intelligence?

Transform your transportation infrastructure with AI-powered predictive capabilities

Schedule Technical Demo