Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac to two real metropolitan transportation networks, namely Chengdu and Beijing, using real traffic data, with satisfying results.

Keywords

Gaussian distribution, Generalized actor critic, Navigation, Optimization, Real-time systems, Reliability, Routing, sample efficiency, stochastic on-time arrival (SOTA), Transportation, variance reduction

Discipline

Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Intelligent Transportation Systems

First Page

1

Last Page

16

ISSN

1524-9050

Identifier

10.1109/TITS.2024.3361445

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TITS.2024.3361445

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