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Author Hughes, S.; Moreno, S.; Yushimito, W.F.; Huerta-Canepa, G. doi  openurl
  Title Evaluation of machine learning methodologies to predict stop delivery times from GPS data Type Journal Article
  Year 2019 Publication Transportation Research Part C-Emerging Technologies Abbreviated Journal Transp. Res. Pt. C-Emerg. Technol.  
  Volume 109 Issue Pages 289-304  
  Keywords Machine learning; Stop delivery time; Classification; Regression; Hazard duration; GPS  
  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 Medellin, 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).  
  Address [Hughes, Sebastian; Moreno, Sebastian; Yushimito, Wilfredo F.; Huerta-Canepa, Gonzalo] Univ Adolfo Ibanez, Fac Engn & Sci, Vina Del Mar, Chile, Email: shughes@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0968-090x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000504780800016 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1082  
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