Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow-Reference-Cited by-同舟云学术

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Mining the spatio‐temporal pattern using matrix factorisation: a case study of traffic flow

Published:2020-09-02 Issue:10 Volume:14 Page:1328-1337
ISSN:1751-956X
Container-title:IET Intelligent Transport Systems
language:en
Short-container-title:IET intell. transp. syst

Author:

Xu Chenyang¹^ORCID,Xu Changqing¹,Truong Trieu‐Kien¹

Affiliation:

1. Shanghai Key Laboratory of Navigation and Location‐Based Services, Shanghai JiaoTong UniversityShanghaiPeople's Republic of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Law,Mechanical Engineering,General Environmental Science,Transportation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-its.2019.0705

Reference28 articles.

1. Macroscopic traffic state estimation using relative flows from stationary and moving observers

2. Real‐time estimation of travel speed using urban traffic information system and filtering algorithm

3. Citywide traffic congestion estimation with social media

4. Short‐term traffic flow prediction using fuzzy information granulation approach under different time intervals

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Analyzing urban traffic crash patterns through spatio-temporal data: A city-level study using a sparse non-negative matrix factorization model with spatial constraints approach;Applied Geography;2024-11

2. Construction of Dynamic Traffic Pattern Recognition and Prediction Model Based on Deep Learning in the Background of Intelligent Cities;IEEE Access;2024

3. Discovering spatiotemporal flow patterns: where the origin–destination map meets empirical orthogonal function decomposition;Cartography and Geographic Information Science;2023-02-21

4. The Problem of Data Mining in Modelling Traffic Flows in a Megapolis;World of Transport and Transportation;2021-02-13

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