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Traffic state estimation on highway: A comprehensive survey
Traffic state estimation (TSE) refers to the process of the inference of traffic state variables
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
(ie, flow, density, speed and other equivalent variables) on road segments using partially …
A survey on data imputation techniques: Water distribution system as a use case
MS Osman, AM Abu-Mahfouz, PR Page - IEEE Access, 2018 - ieeexplore.ieee.org
The presence of missing data is problematic in most quantitative research studies. Water
distribution systems (WDSs) are not immune to this problem. In fact, missing data is an …
distribution systems (WDSs) are not immune to this problem. In fact, missing data is an …
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
The missing data problem is inevitable when collecting traffic data from intelligent
transportation systems. Previous studies have shown the advantages of tensor completion …
transportation systems. Previous studies have shown the advantages of tensor completion …
Memory-augmented dynamic graph convolution networks for traffic data imputation with diverse missing patterns
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Recent research has employed graph neural networks (GNNs) for …
transportation systems. Recent research has employed graph neural networks (GNNs) for …
Missing value imputation for traffic-related time series data based on a multi-view learning method
In reality, readings of sensors on highways are usually missing at various unexpected
moments due to some sensor or communication errors. These missing values do not only …
moments due to some sensor or communication errors. These missing values do not only …
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Rapid advances in sensor, wireless communication, cloud computing and data science
have brought unprecedented amount of data to assist transportation engineers and …
have brought unprecedented amount of data to assist transportation engineers and …
Missing traffic data imputation and pattern discovery with a Bayesian augmented tensor factorization model
Spatiotemporal traffic data, which represent multidimensional time series on considering
different spatial locations, are ubiquitous in real-world transportation systems. However, the …
different spatial locations, are ubiquitous in real-world transportation systems. However, the …
A grey convolutional neural network model for traffic flow prediction under traffic accidents
Y Liu, C Wu, J Wen, X **ao, Z Chen - Neurocomputing, 2022 - Elsevier
Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has
become a trending topic in intelligent transportation systems. However, the occurrence of …
become a trending topic in intelligent transportation systems. However, the occurrence of …
A customized deep learning approach to integrate network-scale online traffic data imputation and prediction
Online data imputation and traffic prediction based on real-time data streams are essential
for the intelligent transportation systems, particularly online navigation applications based …
for the intelligent transportation systems, particularly online navigation applications based …
Graph Markov network for traffic forecasting with missing data
Traffic forecasting is a classical task for traffic management and it plays an important role in
intelligent transportation systems. However, since traffic data are mostly collected by traffic …
intelligent transportation systems. However, since traffic data are mostly collected by traffic …