Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2019

Abstract

We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale up to real world problems. Empirical results on synthetic and real world problems show that our approach can significantly reduce congestion while keeping the traffic throughput high.

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the Thirty-Third Conference on Artificial Intelligence 2019: Honolulu, HI, January 27 – February 1

First Page

6171

Last Page

6178

Identifier

10.1609/aaai.v33i01.33016171

Publisher

AAAI Press

City or Country

Menlo Park, CA

Additional URL

https://doi.org/10.1609/aaai.v33i01.33016171

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