Review of data fusion methods for real-time and multi-sensor traffic flow analysis
Recently, development in intelligent transportation systems (ITS) requires the input of
various kinds of data in real-time and from multiple sources, which imposes additional …
various kinds of data in real-time and from multiple sources, which imposes additional …
A comprehensive survey on imputation of missing data in internet of things
The Internet of Things (IoT) is enabled by the latest developments in smart sensors,
communication technologies, and Internet protocols with broad applications. Collecting data …
communication technologies, and Internet protocols with broad applications. Collecting data …
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 …
Δfree-LSTM: An error distribution free deep learning for short-term traffic flow forecasting
Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …
memory (LSTM) network is considered qualified to capture the long-term temporal …
M3care: Learning with missing modalities in multimodal healthcare data
Multimodal electronic health record (EHR) data are widely used in clinical applications.
Conventional methods usually assume that each sample (patient) is associated with the …
Conventional methods usually assume that each sample (patient) is associated with the …
A multi-view bidirectional spatiotemporal graph network for urban traffic flow imputation
Accurate estimation of missing traffic data is one of the essential components in intelligent
transportation systems (ITS). The non-Euclidean data structure and complex missing traffic …
transportation systems (ITS). The non-Euclidean data structure and complex missing traffic …
Deep learning for road traffic forecasting: Does it make a difference?
Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …
This has also been the case of Intelligent Transportation Systems, in which several areas …
Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data
Urban road travel time estimation and prediction on a citywide scale is a necessary and
important task for recommending optimal travel paths. However, this problem has not yet …
important task for recommending optimal travel paths. However, this problem has not yet …
Missing data imputation for traffic congestion data based on joint matrix factorization
In reality, the missing of some traffic data is inevitable due to some unexpected errors, which
not only affects traffic management but also hinders the development of traffic data research …
not only affects traffic management but also hinders the development of traffic data research …
A novel deep learning approach for anomaly detection of time series data
Z Ji, J Gong, J Feng - Scientific Programming, 2021 - Wiley Online Library
Anomalies in time series, also called “discord,” are the abnormal subsequences. The
occurrence of anomalies in time series may indicate that some faults or disease will occur …
occurrence of anomalies in time series may indicate that some faults or disease will occur …