Towards Maximum Throughput in Generalized Automated Transport Networks

Leveraging the connectivity and automation of vehicles to enhance traffic and logistic control is an important yet challenging topic in future city operations and industrial settings. One of the central questions is how to characterize and optimize the capacity of automated transport systems, a problem complicated by network topology, demand heterogeneity, and stochastic dynamics. Rhythmic control (RC) is a recently proposed scheme that integrates performance guarantees, generality, and conceptual simplicity, thereby enabling a tractable analysis of network-level capacity. This paper undertakes a rigorous theoretical investigation into the capacity of generalized automated transport networks, covering both one-way and two-way configurations. By abstracting infrastructures such as road networks, warehouse aisle systems, and port yard lanes into rhythmic networked systems, RC provides a unified analytical framework for evaluating throughput and stability across diverse automated transport domains. Within a static analytical setting, we formally characterize the notion of demand accommodation associated with a rhythm design. Under a stationary stochastic framework, we establish that RC achieves throughput optimality; however, accommodation alone does not guarantee stabilization under the same rhythm design, and stabilizing certain demand patterns may require excessively long cycle lengths. To mitigate this drawback, we introduce a variant of RC that incorporates strategic holdings. We demonstrate that, under mild conditions, this extension guarantees the stabilization of any accommodatable demand and achieves approximate throughput optimality under finite cycle lengths, with explicit throughput and stability guarantees that support refined rhythm design and capacity analysis. Extensive simulation studies are conducted to evaluate the performances of the proposed RC. The results verify its core effectiveness, demonstrate that a period-wise design enables adaptation to time-varying flows, and show that strategic holdings significantly enhance overall system efficiency.

Funding: This research was supported by the National Natural Science Foundation of China [Grant 72371141].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2025.0407.