Deep Reinforcement Learning for Multi-asset Infrastructure Management Incorporating Traffic Operations Adaptations and Control

Papakonstantinou, K G; Stoffels, S. M.; Miller-Hooks, E.; Saifullah, M

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Deep Reinforcement Learning for Multi-asset Infrastructure Management Incorporating Traffic Operations Adaptations and Control

2023-04-01
By Papakonstantinou, K G ; Stoffels, S. M. ; Miller-Hooks, E.

File Language:

English

Details

Creators:

Papakonstantinou, K G ; Stoffels, S. M. ; Miller-Hooks, E. ; Saifullah, M
Corporate Creators:

Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC)
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS) (UTC)
Abstract:

Preserving structural integrity through inspections and maintenance (I&M) is a critical and recurrent task that involves complex decisions strategically placed in both space and time to ensure the longevity and reliability of infrastructure assets. At the regional scale, the optimal management of entire networks amplifies the complexity of these decisions, primarily due to the vast number of interconnected assets, each with its inherent dependencies and correlations. In the context of transportation networks, the dependencies can manifest as system-level effects on traffic flow, as the individual contributions of various assets to traffic capacity collectively influence system performance, often in a nonlinear manner. Consequently, assessing the potential impacts of different combinations of I&M decisions, each linked to a specific asset, becomes an intricate process.
Format:

PDF
Funding:

69A3551847103
Download URL:

https://rosap.ntl.bts.gov/view/dot/84031/dot_84031_DS1.pdf
File Type:

[PDF - 5.40 MB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers
Main Document Checksum:

urn:sha-512:04c9de96687409f2580a75b8704910943e8567cb6d6cdd1fa4d8178501fc3ddbd173f21be70f19f6723a80e048d029df07c678bc415a30f235f06e6c94755fac

File Language:

English

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