Urban Freight Delivery Stop Identification with GPS Data

Abstract

Delivery stop identification is a crucial but challenging step in the measurement of urban freight performance. This paper presents the application of a robust learning method, support vector machine (SVM), in identifying delivery stops with GPS data. The duration of a stop, the distance from a stop to the center of the city, and the distance to a stop's closest major bottleneck were extracted as the three features used in the SVM model. A linear SVM with nested K-fold cross validation proved to be highly reliable and robust in identifying delivery stops with relatively long stop duration, such as those made for grocery stores. Second-by-second freight GPS data collected in New York City were used to conduct a case study. The identification accuracy for the case study was higher than 99% for the training and testing data sets.