Optimizing Autonomous Transfer Hub Networks: Quantifying the Potential Impact of Self-Driving Trucks

TL;DR

This paper develops a flow-based optimization framework for Autonomous Transfer Hub Networks, demonstrating their potential to significantly improve freight transportation efficiency across the US with autonomous trucks.

Contribution

Introduces a scalable, data-driven optimization model for ATHNs that can evaluate large-scale autonomous trucking benefits efficiently.

Findings

ATHNs can reduce transportation costs significantly.

The framework enables analysis of nationwide autonomous trucking impacts.

Autonomous trucks can improve freight network efficiency.

Abstract

Autonomous trucks are expected to fundamentally transform the freight transportation industry. In particular, Autonomous Transfer Hub Networks (ATHNs), which combine autonomous trucks on middle miles with human-driven trucks on the first and last miles, are seen as the most likely deployment pathway for this technology. This paper presents a framework to optimize ATHN operations and evaluate the benefits of autonomous trucking. By exploiting the problem structure, this paper introduces a flow-based optimization model for this purpose that can be solved by blackbox solvers in a matter of hours. The resulting framework is easy to apply and enables the data-driven analysis of large-scale systems. The power of this approach is demonstrated on a system that spans all of the United States over a four-week horizon. The case study quantifies the potential impact of autonomous trucking and shows that ATHNs can have significant benefits over traditional transportation networks.