Geofence‑Driven Trailer Management System
Revolutionizing trailer logistics with geofence-driven machine learing.
Gain real-time visibility, reduce idle time, and boost fleet efficiency.
ML Transformation: Geofence‑Driven Trailer Management System
Overview
Soulax developed an advanced geospatial machine learning pipeline to optimize drop-and-hook logistics through automated trailer tracking. The solution uses geofence-based event detection and real-time analytics to reduce trailer idle time, increase fleet utilization, and offer live visibility into trailer status at decentralized yards and consignee locations.
ML Architecture
Architecture Diagram:
GPS Pings + Sensor Streams → DBSCAN Clustering → Geofence DB → Real-Time Event Detection (XGBoost) → Notifications + Grafana Dashboard
Situation
Logistics companies often lose visibility over their trailers once they are dropped at consignee or yard locations. This lack of real-time tracking leads to:
- Extended Dwell Time (trailers sitting idle)
- Inefficient Trailer Cycles
- Manual Tracking Dependence
The system aimed to solve these by using ML-driven geofences and unload event prediction, enabling real-time trailer monitoring without on-ground input.
Tasks
Challenges Addressed:
- Unmonitored Drop Locations: No control over trailers post-drop.
- Irregular Dwell Durations: Varying wait times before unload.
- Scalability Needs: Millions of daily GPS + sensor events across 100+ yards.
- Event Detection: Identifying unloads without human input.
Action
Geofence Clustering Using DBSCAN
- Historical GPS Data clustered with DBSCAN to define yard boundaries.
- Each virtual fence is generated using a convex hull and stored in GeoJSON format.
- Silhouette Score and IoU validation used for tuning hyperparameters (eps, min_samples).
Unload Detection Model
- Features Used:
- Time spent in geofence
- Speed variance
- Door sensor trigger
- Time of day
- Weather data
- Model: XGBoost binary classifier
- Precision: 96%
- Recall: 92%
- Door sensor trigger
- Time of day
- Weather dataTraining/validation done on labeled unload events
Event Inference and Alerts
- Real-time GPS/sensor streams pushed via Kinesis Data Streams.
- AWS Lambda triggers predictions; if unload event is detected, alerts sent via:
- SMS
- Webhooks
Visualization Layer
- Grafana Dashboards:
- Yard-wise dwell time trends
- Fence map overlays
- Detected unload count
- Alert history and benchmark comparisons
Scalability & Security
- Preprocessing with AWS Glue ensures timestamp and geo-coord normalization.
- Data stored securely in Amazon S3 and RDS with IAM-based access control.
- Data stored securely in Amazon S3 and RDS with IAM-based access control.
- Model drift
- Data quality
- Alert failures
Scalability & Security
Metric | Impact |
Dwell Time Reduction | ↓ 20% |
Detection Accuracy (Live) | 96% Precision / 92% Recall |
Trailer Utilization Uplift | ↑ 15% more cycles/week |
Customer Alert Latency | < 5 seconds per event |
Conclusion
Soulax’s geofence‑driven ML system brings automation and real-time visibility to trailer logistics. It reduced idle time, improved asset utilization, and enabled better yard planning — all without relying on manual tracking or reporting.
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