  City-wide package delivery becomes popular due to the dramatic rise of online
shopping. It places a tremendous burden on the traditional logistics industry,
which relies on dedicated couriers and is labor-intensive. Leveraging the
ridesharing systems is a promising alternative, yet existing solutions are
limited to one-hop ridesharing or need consignment warehouses as relays. In
this paper, we propose a new package delivery scheme which takes advantage of
multi-hop ridesharing and is entirely consignment free. Specifically, a package
is assigned to a taxi which is guided to deliver the package all along to its
destination while transporting successive passengers. We tackle it with a
two-phase solution, named \textbf{PPtaxi}. In the first phase, we use the
Multivariate Gauss distribution and Bayesian inference to predict the passenger
orders. In the second phase, both the computation efficiency and solution
effectiveness are considered to plan package delivery routes. We evaluate
\textbf{PPtaxi} with a real-world dataset from an online taxi-taking platform
and compare it with multiple benchmarks. The results show that the successful
delivery rate of packages with our solution can reach $95\%$ on average during
the daytime, and is at most $46.9\%$ higher than those of the benchmarks.
