Physical Internet Enabled Autonomous Trucking System: Modular Containerization and Fuel-Efficient Planning

Abstract

Autonomous trucking, particularly in the domain of truck platooning, is an emerging technology with the potential to revolutionize the freight transportation sector. Beyond addressing the shortage of truck drivers, autonomous truck platooning aims to significantly reduce fuel costs and emissions. This reduction is achieved through two main mechanisms: (1) operating in close platoon formation substantially decreases air drag, and (2) the absence of labor costs associated with drivers shifts the route optimization objective from minimizing travel time to minimizing fuel consumption. Motivated by the innovative concept of the Physical Internet, this paper focuses on designing a paradigm for an autonomous trucking system within a hyperconnected transportation network. The goal is to minimize fuel consumption while ensuring a 99% robust on-time delivery rate under stochastic conditions. We propose a data-driven, two-stage stochastic optimization model. In the first stage, flexible modular containers are designed based on historical data, and truck quantities and sizes are strategically deployed at each hub. In the second stage, an algorithm dynamically adjusts truck transfers between hubs based on real-time demand and traffic forecasts, determining optimal daily platooning routes. Lastly, the model’s energy efficiency and robustness are validated using real-world cargo transport data.