Weigh-in-Motion: Sensor Integration, Data Analytics & Predictive Modeling
Weigh-in-Motion: Sensor Integration, Data Analytics & Predictive Modeling
Business Overview
A large transportation authority required a centralized digital solution to ingest, process, visualize, and analyze massive volumes of real-time traffic data captured from Weigh-in-Motion (WiM) sensors installed across highway networks. The objective was to enable automated monitoring, performance insights, and predictive modeling for proactive infrastructure management and traffic optimisation.
Challenge
The authority operated 87 WiM stations generating continuous high-volume datasets over multiple years. Key challenges included:
- Fragmented sensor and camera data streams with no unified processing mechanism
- Need for near real-time analysis and alerting for events such as overspeeding and classification anomalies
- Lack of advanced analytical visualization for operations, maintenance, and traffic engineering teams
- Requirement for predictive capabilities to anticipate pavement wear, traffic variations, and maintenance needs
- Need for a scalable, secure, and administration-friendly platform to manage users, configurations, alerts, and data access
Solution
The team delivered an end-to-end WiM data management and analytics platform integrating hardware, data engineering, and machine learning models.
- Sensor & Data Integration
- Raspberry Pi-based interface to capture live vehicle data and images from WiM sensors across multiple lanes
- High-frequency traffic feed ingestion and synchronization with camera streams
- PostgreSQL database with partitioning for high-volume, long-term datasets
- Automated Data Processing & Alerts
- Processing rule-based logic for each vehicle event (speed, weight, class, axle count, temperature, spacing, etc.)
- Real-time email, dashboard notifications, and SMS alerts for defined thresholds (e.g., speed violations)
- Visualization & Operational Tools
- Web-based administration console for:
- Access control
- Live feed monitoring
- Alert management
- Report viewing
- WiM binary file interpretation
- Historical traffic analytics and querying
- KPI dashboard visualizing trends across stations, vehicle categories, and conditions
- Web-based administration console for:
- Predictive Modeling
- Regression and ML models developed using Amazon ML platform to predict:
- Road wear and pavement fatigue
- Traffic demand fluctuations
- Preventive maintenance requirements
- Regression and ML models developed using Amazon ML platform to predict:
Impact
- Real-Time Operational Awareness: Sensor data integrated with automated alerts enabled faster response to anomalies and violations.
- Data-Driven Maintenance: Predictive modeling empowered proactive pavement maintenance planning and infrastructure investment decisions.
- Improved Reporting & Transparency: Dynamic dashboards gave stakeholders easy access to live and historical insights across all WiM stations.
- High Scalability & Future Readiness: Architecture supports ongoing expansion to additional stations, data models, and traffic intelligence use cases.
- Reduced Manual Intervention: Full automation, from data capture to processing and reporting, significantly reduced operational effort and human dependency.
SHARE
