Evaluation of machine learning methodologies to predict stop delivery times from GPS data. (December 2019)

• Record Type: Journal Article
• Authors: Hughes, Sebastián; Moreno, Sebastián; Yushimito, Wilfredo F.; Huerta-Cánepa, Gonzalo
• Is Part Of: Transportation research. Volume 109(2019)
• Page Start: 289
• Page End: 304
• Publication Date: 2019-12
• Subjects: Machine learning -- Stop delivery time -- Classification -- Regression -- Hazard duration -- GPS; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.10.018
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Regression models (RM) and classification models are be used to predict the stop delivery time (SDT). Based on MAPE, hazard duration models (HDM) are recommended over RM for SDT predictions. K-nearest neighbor (KNN) provides the best results when predicting if SDT exceeds a time threshold. A two level model (classification -KNN- and regression/hazard duration) improves the prediction the stop delivery time (SDT). Abstract: In last mile distribution, logistics companies typically arrange and plan their routes based on broad estimates of stop delivery times (i.e., the time spent at each stop to deliver goods to final receivers). If these estimates are not accurate, the level of service is degraded, as the promised time window may not be satisfied. The purpose of this work is to assess the feasibility of machine learning techniques to predict stop delivery times. This is done by testing a wide range of machine learning techniques (including different types of ensembles) to (1) predict the stop delivery time and (2) to determine whether the total stop delivery time will exceed a predefined time threshold (classification approach). For the assessment, all models are trained using information generated from GPS data collected in Medellín, Colombia and compared to hazard duration models. The results are threefold. First, the assessment shows that regression-based machine learning approaches are not better than conventional hazard duration models concerning absolute errors of the prediction of the stop delivery times. Second, when the problem is addressed by a classification scheme in which the prediction is aimed to guide whether a stop time will exceed a predefined time, a basic K-nearest-neighbor model outperforms hazard duration models and other machine learning techniques both in accuracy and F 1 score (harmonic mean between precision and recall). Third, the prediction of the exact duration can be improved by combining the classifiers and prediction models or hazard duration models in a two level scheme (first classification then prediction). However, the improvement depends largely on the correct classification (first level).