An On-Demand Dynamic Taxi Crowdshipping Model for Urban Parcel Delivery

Abstract In this article, a dynamic crowdshipping model is presented to use the vacant space of taxis to deliver parcels while serving passengers. A mixed integer linear programming (MILP) model within a rolling horizon framework that periodically updates input information is formulated. The objective function aims to maximize the number of matched trips while controlling the total travel time in the system. The developed model is tested using the taxi ridership and the household travel survey data in New York City, ensuring the credibility of the proposed model in addressing the goals of the study and its potential application to mitigate transportation externalities.