PPtaxi: Non-stop Package Delivery via Multi-hop Ridesharing
Yueyue Chen, Deke Guo, Ming Xu, Guoming Tang, Tongqing Zhou, Bangbang, Ren

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…
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Taxonomy
TopicsTransportation and Mobility Innovations · Sharing Economy and Platforms · Smart Parking Systems Research
