Industry-standard "day-of arrival" ETA accuracy is ~45%. Customer service teams field hundreds of "where's my truck?" calls daily. Receivers can't plan dock labor. Predictive ETA replaces wishful thinking with accurate windows.
Models ingest 100+ features per shipment (GPS telemetry, AIS vessel data, historical transit, traffic, weather, port congestion, driver HOS, carrier behavior). Gradient-boosted trees, deep neural nets, and mixture-of-experts architectures produce arrival distributions with confidence intervals. Continuous retraining handles drift.
ML modeling pipeline + telemetry ingestion layer + traffic/weather/AIS feeds + retraining orchestration + customer notification API.
Central TMS orchestrating route planning, carrier selection, consolidation, execution, and settlement on a single rate-table-driven engine.
Shipment data and transit history required as model features.
Electronic Logging Devices auto-capturing engine status, GPS, and HOS compliance from the ECM — mandated for ~3.4M US commercial drivers.
HOS and location telemetry required for in-transit ETA signals.
Cross-modal tracking platform aggregating GPS, ELD, AIS, and IoT into a normalized feed with predictive ETA, exception alerts, and control tower.
Carrier telematics and AIS feeds required as primary model inputs.
Nothing downstream yet.
Your brand here — Reach our audience of professional directory owners and boost your sales.