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

arXiv: https://arxiv.org/abs/2305.03119 · HTML: https://mlsanity.com/paper/2305.03119/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2023.

## Built on by (most cited first, 1 of 1)

- [PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning](https://mlsanity.com/paper/2608.01201/) (2026)

## Abstract (arXiv)

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.