Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges
Author:
Affiliation:
1. School of Engineering, Monash University Malaysia, Bandar Sunway, Selangor, Malaysia
2. Department of Electrical and Computer Systems Engineering, Faculty of Engineering, Monash University, Clayton, VIC, Australia
Funder
Malaysian Ministry of Higher Education (MOHE), Fundamental Research Grant Scheme (FRGS), through the purview of Monash University Malaysia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10091533.pdf?arnumber=10091533
Reference101 articles.
1. Data denoising and compression of intelligent transportation system based on two‐dimensional discrete wavelet transform
2. Outlier detection in traffic data set
3. An empirical survey of data augmentation for time series classification with neural networks
4. DxNAT—Deep neural networks for explaining non-recurring traffic congestion;sun;Proc IEEE Int Conf Big Data,2018
5. Missing data imputation for traffic flow based on combination of fuzzy neural network and rough set theory
Cited by 9 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. An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden;Data Science for Transportation;2024-09-13
2. Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras;Electronics;2024-07-22
3. Parameter-Transferred Irreducible LSTM for Traffic Data Imputation;IEEE Sensors Journal;2024-07-15
4. Urban Traffic Management for Reduced Emissions: AI-based Adaptive Traffic Signal Control;2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS);2024-07-10
5. Deep Learning Missing Value Imputation on Traffic Data Using Self-Attention and GAN-based Methods;2024 Panhellenic Conference on Electronics & Telecommunications (PACET);2024-03-28
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