A Deep Reinforcement Learning Model for a Two-Layer Scheduling Policy in Urban Public Resources-Reference-Cited by-同舟云学术

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A Deep Reinforcement Learning Model for a Two-Layer Scheduling Policy in Urban Public Resources

Published:2024-01-15 Issue:2 Volume:11 Page:2712-2727
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhang Cong¹^ORCID,Wu Fan²^ORCID,Wang He³^ORCID,Zhang Hegeng²^ORCID,Ma Huadong¹^ORCID,Liu Yuanan²^ORCID

Affiliation:

1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China

2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China

3. International School, Beijing University of Posts and Telecommunications, Beijing, China

Funder

National Natural Science Foundation of China

Beijing Natural Science Foundation Project

Beijing Key Laboratory of Work Safety Intelligent Monitoring

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10384482/10183782.pdf?arnumber=10183782

Reference41 articles.

1. Dockless bike-sharing systems: what are the implications?

2. Bicycle sharing system design with capacity allocations

3. Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction

4. DNEAT: A novel dynamic node-edge attention network for origin-destination demand prediction

5. Multi-community passenger demand prediction at region level based on spatio-temporal graph convolutional network

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