Shipment status prediction in online crowd-sourced shipping platforms

This paper empirically studies the matching and delivery process in a major crowd-sourced delivery platform. The aim is to develop models to understand and predict crowd-shipping delivery performance and using the findings to design incentives to improve user experiences as well as system performance. We apply the random forest machine learning algorithm to predict the shipment status of 14,858 crowd-shipping requests recorded between January 2015 and December 2016 throughout the U.S. The models are used to predict three phases of the crowd-shipping performance, namely bidding, acceptance, and delivery, using shipping request, built-environment, and socioeconomic features as explanatory variables. The results demonstrate that the context of the shipment provides strong predictive performance even when shipping request and package information is unknown. Calculating the sensitivity of bid probability, we show that offering a higher reward and posting a shipping request in the morning has the largest effect on the probability to secure a bid. We also find that larger shipments, out-of-state destinations, and peer-to-peer shipments lead to higher sensitivity, likely reflecting the higher perceived risks of such transactions. In practice, the models presented in this study show promise in their ability to effectively predict shipment status in real time. We illustrate a valuable application of the sensitivity analysis derived from the random forest models to develop customer-tailored crowd-shipping smartphone applications. Based on the data mined from past deliveries, customers are given empirically based delivery forecasts for their specific package request and can modify delivery requests to increase their odds of delivery. We find that pricing is the variable with the highest potential to increase delivery probability followed by the timing of the request.