Machine Learning-Based Approach for Delivery Cycle State Classification in Production Metrics: A Comparative Study

Global Positioning System (GPS) technology has significantly enhanced logistic activities in recent years by the precise and realtime monitoring of vehicle locations. Along with real-time location monitoring, gaining more informative data about vehicle activity has become increasingly vital for additional optimization. This is particularly the case for large-scale production processes such as street paving, where multiple vehicles are employed and the highly accurate coordination of their activities significantly impacts overall productivity and quality. While previous studies have predominantly focused on truck stop classification and delivery modes, this study suggests a novel approach to segment and classify vehicle activity along the entire delivery process into distinct operational phases: loading, traveling, waiting to unload, and unloading. A real-world GPS trajectory dataset of 165 transporters and 6,144 delivery trips was utilized to generate a feature-engineered dataset through rule-based preprocessing steps. Diverse machine learning models were fine-tuned and compared based on accuracy and F 1 Score to ascertain classification performance. The highest accuracy of 98% was achieved with the Random Forest (RF) classifier, demonstrating the potential of the model for accurately and automatically estimating vehicle states from available GPS data. The findings demonstrate the effectiveness of data-driven approaches in streamlining feet management and operational efficiency.