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

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 offline to build the package transport network. In the second phase, we develop an online 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.