A spatiotemporal approach for traffic data imputation with complicated missing patterns-Reference-Cited by-同舟云学术

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A spatiotemporal approach for traffic data imputation with complicated missing patterns

Published:2020-10 Issue: Volume:119 Page:102730
ISSN:0968-090X
Container-title:Transportation Research Part C: Emerging Technologies
language:en
Short-container-title:Transportation Research Part C: Emerging Technologies

Author:

Li Huiping,Li Meng,Lin Xi,He Fang,Wang Yinhai

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Anhui Science and Technology Major Project

Science & Technology Program of Beijing

Publisher

Elsevier BV

Subject

Computer Science Applications,Transportation,Automotive Engineering,Civil and Structural Engineering

Reference40 articles.

1. Model Selection and Multi-model Inference;Anderson,2004

2. Multiple imputation for continuous variables using a Bayesian principal component analysis;Audigier;J. Stat. Comput. Simul.,2016

3. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm;Aydilek;Inf. Sci.,2013

4. Missing data imputation for traffic flow speed using spatio-temporal cokriging;Bae;Transport. Res. Part C: Emerg. Technol.,2018

5. An analysis of four missing data treatment methods for supervised learning;Batista;Appl. Artif. Intell.,2003

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