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Physics-Informed Deep Reinforcement Learning for Resilient and Congestion-Aware Transportation Network Recovery

https://doi.org/10.2139/ssrn.6561872
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Prioritizing post-earthquake recovery of transportation networks under uncertainty: A deep reinforcement learning approach
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Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem
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Framework for improving the resilience and recovery of transportation networks under geohazard risks
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A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes
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Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
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Resilience-based risk mitigation for road networks
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Model-free reinforcement learning with model-based safe exploration: Optimizing adaptive recovery process of infrastructure systems
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An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand
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Deep Reinforcement Learning for Crowdsourced Urban Delivery
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Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach
inferredcites →https://doi.org/10.1016/j.trc.2019.01.014openalex-snapshot
Resilience in Intelligent Transportation Systems (ITS)
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Learning routing policies in a disrupted, congestible network with real-time information: An experimental approach
inferredcites →https://doi.org/10.1016/j.trc.2022.103981openalex-snapshot
A deep reinforcement learning approach for solving the Traveling Salesman Problem with Drone
inferredcites →https://doi.org/10.1038/s42256-020-0177-2openalex-snapshot
Finding key players in complex networks through deep reinforcement learning
inferredcites →https://doi.org/10.1061/(asce)is.1943-555x.0000212openalex-snapshot
Measuring the Performance of Transportation Infrastructure Systems in Disasters: A Comprehensive Review
inferredcites →https://doi.org/10.1061/(asce)me.1943-5479.0000868openalex-snapshot
Prioritizing Postdisaster Recovery of Transportation Infrastructure Systems Using Multiagent Reinforcement Learning
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Leveraging Deep Reinforcement Learning for Water Distribution Systems with Large Action Spaces and Uncertainties: DRL-EPANET for Pressure Control
inferredcites →https://doi.org/10.1080/23789689.2018.1448663openalex-snapshot
Resilience metrics and measurement methods for transportation infrastructure: the state of the art
inferredcites →https://doi.org/10.1109/tits.2020.3008612openalex-snapshot
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
inferredcites →https://doi.org/10.1109/tpwrs.2021.3102870openalex-snapshot
Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning
inferredcites →https://doi.org/10.1111/mice.12813openalex-snapshot
A graph convolution network‐deep reinforcement learning model for resilient water distribution network repair decisions
inferredcites →https://doi.org/10.1126/sciadv.1701079openalex-snapshot
Resilience and efficiency in transportation networks
inferredcites →https://doi.org/10.1177/03611981241250339openalex-snapshot
Recovery Scheduling of Road Networks Considering Day-to-Day Flow Evolution
inferredcites →https://doi.org/10.1504/ijcis.2014.066356openalex-snapshot
Optimal recovery sequencing for enhanced resilience and service restoration in transportation networks
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