CrowdExpress: A Probabilistic Framework for On-Time Crowdsourced Package Deliveries

Abstract

Most of current urban logistic systems fail to strike a nice trade-off between speed and cost. An express logistic service often implies a high delivery cost. Crowdsourced logistics is a promising solution to alleviating such contradiction. In this article, we propose a new form of crowdsourced logistics that organizes passengers and packages in a shared room, i.e., using taxis that are already transporting passengers as package hitchhikers to achieve on-time deliveries. It is well-recognized that taxi drivers are good at delivering passengers to their destinations efficiently. As a result, the proposed new urban logistics system has potentials to lower the cost and accelerate package deliveries simultaneously. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express service. In the first phase, we mine the historical taxi GPS trajectory data <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> to build the package transport network. In the second phase, we develop an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> taxi scheduling algorithm to adaptively discover the path with the maximum arriving-on-time probability “on-the-fly” upon real-time passenger-sending requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be successfully delivered daily on time with the success rate over 94 percent.