Abstract

The study of vehicle stops in last-mile delivery has gained ground in the specialized logistics literature. An efficient last-mile delivery reduces distribution costs and mitigates negative externalities such as pollution and congestion. This paper estimates the stops of last-mile trucks that deliver food products in Santiago, Chile. The aim is to study last-mile delivery operations using a non-intrusive, low-cost method. Particularly, we devise a novel methodology that employs multiple data sources to detect the primary stops of cargo vehicles. The proposed methodology involves the following two steps. First, we use GPS data to identify all the candidates for stops, that is, clusters of points close to each other in terms of distance and time, and then, these stops are classified as primary according to the proximity to planned visits or check-out markings. Finally, we conduct a case study involving food distribution, deriving managerial and public policy insights. We find that the most adequate time threshold to detect stops in our context is 4 min, which is considerably lower than previous studies. This last may be explained by the last-mile nature of our study. Our results show that primary (i.e., delivery) stops are concentrated mainly in the center of Santiago, with a duration that decreases as the hours go by. This last means that some of the externalities caused by truck stops (e.g., road capacity reduction) are exacerbated during the morning rush hour. We also find that the average duration of the primary stops is 12.5 min, while the mean distance traveled between two consecutive stops is 4.68 km.