PPtaxi: Non-stop Package Delivery via Multi-hop Ridesharing

TL;DR

PPtaxi introduces a multi-hop ridesharing-based package delivery system that is consignment-free, using predictive modeling and route planning to achieve a high success rate in real-world urban environments.

Contribution

It presents a novel multi-hop ridesharing package delivery scheme with a two-phase solution involving predictive passenger modeling and efficient route planning, surpassing existing methods.

Findings

Achieves up to 95% delivery success rate during daytime.

Outperforms benchmarks with up to 46.9% higher success rate.

Validates effectiveness using real-world taxi platform data.

Abstract

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.