Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning-Reference-Cited by-同舟云学术

Optimizing Systemic Redundancy of Traffic Sensor Networks While Maintaining Resilience: New Evidence From Using Graph Learning

Published:2023-09 Issue:3 Volume:17 Page:4567-4578
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Tang Junqing¹,Wei Shufen²^ORCID,Li Duo³^ORCID,Li Xiaowei⁴^ORCID

Affiliation:

1. School of Urban Planning and Design, Shenzhen Graduate Schoole, Peking University, Shenzhen, China

2. School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen, China

3. Department of Engineering, Nottingham Trent University, Nottingham, U.K.

4. School of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an, China

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10235272/10089193.pdf?arnumber=10089193

Reference56 articles.

1. Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach

2. Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

3. How much does it cost annually to maintain ANPR cameras operated by transport for London?,2022

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

5. VLUC: An empirical benchmark for video-like urban computing on citywide crowd and traffic prediction;jiang,2019

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