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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
face-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:

    1. Extended Dwell Time (trailers sitting idle)
    2. Inefficient Trailer Cycles 
    3. 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
    • Email 
    • 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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