Performance analysis of crowd-shipping in urban and suburban areas

Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby offering potential for economic, social, and environmental benefits. Despite the promise of this new logistics model, little is known about its actual functioning, performance, and impact. This paper presents a pioneering study of the performance of a real crowd-shipping system in the U.S. using empirical data from 2 years of operations. We contribute to the literature by: (1) defining performance metrics and developing models that account for the specificity of crowd-shipping systems by distinguishing the essential stages from bidding to acceptance and delivery of shipments, (2) identifying the significant covariates, including shipment features, built environment, and socio-demographic factors giving rise to different delivery performance outcomes, and (3) deriving sensitivity analysis to study the performance and implications of crowd-shipping in urban and suburban areas. The analysis is formalized as two-level nested logit models with nests representing bidding and delivery outcomes. The results show that not only does the delivery outcome performance vary significantly between urban and suburban areas, but the explanatory factors also vary significantly for the two contexts. Additionally, several factors have ambiguous impacts depending on the stage. Larger shipment size (versus strict deadlines) leads to increasing (decreasing) the likelihood of bids being placed, while having the opposite effect when it comes to the delivery phase. The findings highlight the need for developing different strategies to foster and improve the performance of this novel system depending on both the urban–suburban shipping context and the stage of delivery.