Detection of clusters in traffic networks based on spatio‐temporal flow modeling-Reference-Cited by-同舟云学术

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Detection of clusters in traffic networks based on spatio‐temporal flow modeling

Published:2019-02-27 Issue:2 Volume:23 Page:312-333
ISSN:1361-1682
Container-title:Transactions in GIS
language:en
Short-container-title:Transactions in GIS

Author:

Shi Yan¹²^ORCID,Deng Min¹^ORCID,Gong Jianya²,Lu Chang‐Tien³,Yang Xuexi¹,Liu Huimin¹

Affiliation:

1. Department of Geo‐informatics Central South University Changsha China

2. State Key Laboratory of Information Engineering in Surveying, Mapping & Remote Sensing Wuhan University Wuhan China

3. Department of Computer Science Virginia Tech Falls Church Virginia USA

Funder

National Key Research and Development Foundation of China

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.12521

Reference35 articles.

1. Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks

2. ST-DBSCAN: An algorithm for clustering spatial–temporal data

3. Pattern Discovering of Regional Traffic Status with Self-Organizing Maps

4. Spatio-Temporal Clustering of Road Network Data

5. Spatio-temporal autocorrelation of road network data

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1. Interpretable Clustering of Spatio-Temporal Data by using SHAP Method;2024 10th International Conference on Web Research (ICWR);2024-04-24

2. Spatiotemporal Flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data;International Journal of Geographical Information Science;2023-05-08

3. Spatial-temporal adaptive network partitioning for urban traffic signal control;Journal of Physics: Conference Series;2023-04-01

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

5. Multi-Criteria Decision-Making for Sustainable Transport: A Case Study on Traffic Flow Prediction Using Spatial–Temporal Traffic Sequence;International Series in Operations Research & Management Science;2023

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