Fetching the paper…
Reading the bibliography…
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems.
Exploring imputation techniques for missing data in transportation management systems
Brian L Smith, William T Scherer, and James H Conklin · 2003
Earlier work this paper cites.
Estimation of missing traffic counts using factor, genetic, neural, and regression techniques
Ming Zhong, Pawan Lingras, and Satish Sharma · 2004
Earlier work this paper cites.
Markov chain monte carlo multiple imputation using bayesian networks for incomplete intelligent transportation systems data
Daiheng Ni and John D Leonard · 2005
Earlier work this paper cites.
Ppca-based missing data imputation for traffic flow volume: A systematical approach
Li Qu, Li Li, Yi Zhang, and Jianming Hu · 2009
Earlier work this paper cites.
Missing traffic flow data prediction using least squares support vector machines in urban arterial streets
Yang Zhang and Yuncai Liu · 2009
Earlier work this paper cites.
Low-dimensional models for missing data imputation in road networks
Muhammad Tayyab Asif, Nikola Mitrovic, Lalit Garg, Justin Dauwels, and Patrick Jaillet · 2013
Earlier work this paper cites.
A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm
Ibrahim Berkan Aydilek and Ahmet Arslan · 2013
Earlier work this paper cites.
Efficient missing data imputing for traffic flow by considering temporal and spatial dependence
Li Li, Yuebiao Li, and Zhiheng Li · 2013
Earlier work this paper cites.
Big data and its technical challenges
Hosagrahar V Jagadish, Johannes Gehrke, Alexandros Labrinidis, Yannis Papakonstantinou, Jignesh M Patel, Raghu Ramakrishnan, and Cyrus Shahabi · 2014
Earlier work this paper cites.
Bidirectional recurrent neural networks as generative models
Mathias Berglund, Tapani Raiko, Mikko Honkala, Leo Kärkkäinen, Akos Vetek, and Juha T Karhunen · 2015
Earlier work this paper cites.
Data-driven imputation method for traffic data in sectional units of road links
Sehyun Tak, Soomin Woo, and Hwasoo Yeo · 2016
Earlier work this paper cites.
Tensor based missing traffic data completion with spatial–temporal correlation
Bin Ran, Huachun Tan, Yuankai Wu, and Peter J Jin · 2016
Earlier work this paper cites.
An efficient realization of deep learning for traffic data imputation
Yanjie Duan, Yisheng Lv, Yu-Liang Liu, and Fei-Yue Wang · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Cited alongside, same era.
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2017
Cited alongside, same era.
Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2017
Cited alongside, same era.
On the imputation of missing data for road traffic forecasting: New insights and novel techniques
Ibai Laña, Ignacio Iñaki Olabarrieta, Manuel Vélez, and Javier Del Ser · 2018
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model
Linchao Li, Bowen Du, Yonggang Wang, Lingqiao Qin, and Huachun Tan · 2020
Later among the works it cites.
Traffic data imputation using deep convolutional neural networks
Ouafa Benkraouda, Bilal Thonnam Thodi, Hwasoo Yeo, Monica Menendez, and Saif Eddin Jabari · 2020
Later among the works it cites.
Inductive graph neural networks for spatiotemporal kriging
Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, and Lijun Sun · 2020
Later among the works it cites.
Gman: A graph multi-attention network for traffic prediction
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi · 2020
Later among the works it cites.
A spatiotemporal approach for traffic data imputation with complicated missing patterns
Huiping Li, Meng Li, Xi Lin, Fang He, and Yinhai Wang · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Missing value imputation for traffic-related time series data based on a multi-view learning method
Linchao Li, Jian Zhang, Yonggang Wang, and Bin Ran · 2018
Cited alongside, same era.
Missing data imputation for traffic flow speed using spatio-temporal cokriging
Bumjoon Bae, Hyun Kim, Hyeonsup Lim, Yuandong Liu, Lee D Han, and Phillip B Freeze · 2018
Cited alongside, same era.
A bayesian tensor decomposition approach for spatiotemporal traffic data imputation
Xinyu Chen, Zhaocheng He, and Lijun Sun · 2019
Cited alongside, same era.
A convolution recurrent autoencoder for spatio-temporal missing data imputation
Reza Asadi and Amelia Regan · 2019
Cited alongside, same era.
Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting
Zulong Diao, Xin Wang, Dafang Zhang, Yingru Liu, Kun Xie, and Shaoyao He · 2019
Cited alongside, same era.
Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
Cited alongside, same era.
Statistical analysis with missing data
Roderick JA Little and Donald B Rubin · 2019
Cited alongside, same era.
Multi-stgcnet: A graph convolution based spatial-temporal framework for subway passenger flow forecasting
Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu · 2020
Later among the works it cites.
St-grat: A novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed
Cheonbok Park, Chunggi Lee, Hyojin Bahng, Yunwon Tae, Seungmin Jin, Kihwan Kim, Sungahn Ko, and Jaegul Choo · 2020
Later among the works it cites.
Graph markov network for traffic forecasting with missing data
Zhiyong Cui, Longfei Lin, Ziyuan Pu, and Yinhai Wang · 2020
Later among the works it cites.
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
Later among the works it cites.
Spatial-temporal traffic data imputation via graph attention convolutional network
Yongchao Ye, Shiyao Zhang, and James J. Q. Yu · 2021
Closest in time.
Bayesian temporal factorization for multidimensional time series prediction
Xinyu Chen and Lijun Sun · 2021
Closest in time.
Missing data repairs for traffic flow with self-attention generative adversarial imputation net
Weibin Zhang, Pulin Zhang, Yinghao Yu, Xiying Li, Salvatore Antonio Biancardo, and Junyi Zhang · 2021
Closest in time.
Yuebing Liang and Zhan Zhao · 2021
Closest in time.