Incorporating multimodal context information into traffic speed forecasting through graph deep learning-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Incorporating multimodal context information into traffic speed forecasting through graph deep learning

Published:2023-07-18 Issue:9 Volume:37 Page:1909-1935
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Zhang Yatao¹²^ORCID,Zhao Tianhong³^ORCID,Gao Song⁴^ORCID,Raubal Martin¹²^ORCID

Affiliation:

1. Future Resilient Systems, Singapore-ETH Centre, ETH Zurich, Singapore

2. Institute of Cartography and Geoinformation, ETH Zurich, Zurich, Switzerland

3. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China

4. Geospatial Data Science Lab, Department of Geography, University of Wisconsin-Madison, Madison, WI, USA

Funder

National Research Foundation Singapore

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2023.2234959

Reference60 articles.

1. Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

2. A recurrent neural network for urban long-term traffic flow forecasting

3. Context-Aware Similarity of Trajectories

4. GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences

Cited by 11 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. Urban streets profiling with coupled spatio-temporal characteristics and topological information from the biking perspective;Computers, Environment and Urban Systems;2024-10

2. Enhancement of traffic forecasting through graph neural network-based information fusion techniques;Information Fusion;2024-10

3. Jointly spatial-temporal representation learning for individual trajectories;Computers, Environment and Urban Systems;2024-09

4. Integrating Spatio-Temporal Graph Convolutional Networks with Convolutional Neural Networks for Predicting Short-Term Traffic Speed in Urban Road Networks;Applied Sciences;2024-07-12

5. Spatio-temporal graph neural networks for missing data completion in traffic prediction;International Journal of Geographical Information Science;2024-07

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号京ICP备18003416号-3