Carbon FootPrint Tracking in Logistics System: AI-Based Logistics Management Platform

Abstract: A previous survey paper by the present authors proposed a web-based Carbon Footprint Tracking System for Logistics, combining ML-based emission prediction, eco-route optimization, shipment-level tracking, and future emission forecasting, to close gaps identified across six recent studies on AI-driven logistics carbon management. This paper evaluates the subsequently completed implementation, CarbonTrack, directly against its own source code, trained models, automated test suite, and deployment configuration rather than the earlier proposal's expectations. The platform is a React/Flask/SQLAlchemy application with 113 REST endpoints, 21 database tables, and six trained regression/classification models (Random Forest and XGBoost) covering fuel consumption, CO2 emission, fuel cost, eco score, predictive maintenance, and route recommendation, integrated with live Google Maps/Routes/Places and OpenWeather APIs. Re-executing the shipped training pipeline reproduced a predictive-maintenance accuracy of 87.5% (weighted F1 = 0.871) and a route-recommendation accuracy of 91.0%, with regression MAEs of 2.02 L (fuel), 3.68 kg (CO2), and 7.26 points (eco score) on an 80/20 held-out split. Re-executing the project's own 234-test suite reproduced 227 passing tests (97.0%). The paper discloses that ML training targets are generated by deterministic formulas from the same input features rather than independent real-world telemetry, and that the previously proposed emission-forecasting feature was not found implemented, so that measured results are not overstated as field-validated performance.

Keywords: Carbon Footprint Tracking, Logistics, Machine Learning, Random Forest, XGBoost, Predictive Maintenance, Route Recommendation, Fleet Management, Experimental Evaluation