Joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values
Multivariate time series (MTS) forecasting is widely used in various domains, such as
meteorology and traffic. Due to limitations on data collection, transmission, and storage, real …
meteorology and traffic. Due to limitations on data collection, transmission, and storage, real …
Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges
With the emerging concepts of smart cities and intelligent transportation systems, accurate
traffic sensing and prediction have become critically important to support urban …
traffic sensing and prediction have become critically important to support urban …
Spatial–temporal multi-feature fusion network for long short-term traffic prediction
Y Wang, Q Ren, J Li - Expert Systems with Applications, 2023 - Elsevier
Exploiting deep spatial–temporal features for traffic prediction has become growing
widespread. Accurate traffic prediction is still challenging due to the complex spatial …
widespread. Accurate traffic prediction is still challenging due to the complex spatial …
MDTP: A multi-source deep traffic prediction framework over spatio-temporal trajectory data
Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in
traffic management, urban computing, public safety, and so on. Recently, the availability of …
traffic management, urban computing, public safety, and so on. Recently, the availability of …
Edge computing-empowered large-scale traffic data recovery leveraging low-rank theory
Intelligent Transportation Systems (ITSs) have been widely deployed to provide traffic
sensing data for a variety of smart traffic applications. However, the inevitable and …
sensing data for a variety of smart traffic applications. However, the inevitable and …
Dynamic multi-view graph neural networks for citywide traffic inference
Accurate citywide traffic inference is critical for improving intelligent transportation systems
with smart city applications. However, this task is very challenging given the limited training …
with smart city applications. However, this task is very challenging given the limited training …
Network-wide traffic states imputation using self-interested coalitional learning
Accurate network-wide traffic state estimation is vital to many transportation operations and
urban applications. However, existing methods often suffer from the scalability issue when …
urban applications. However, existing methods often suffer from the scalability issue when …
Fast attributed multiplex heterogeneous network embedding
In recent years, heterogeneous network representation learning has attracted considerable
attentions with the consideration of multiple node types. However, most of them ignore the …
attentions with the consideration of multiple node types. However, most of them ignore the …
Spatial data quality in the Internet of Things: Management, exploitation, and prospects
With the continued deployment of the Internet of Things (IoT), increasing volumes of devices
are being deployed that emit massive spatially referenced data. Due in part to the dynamic …
are being deployed that emit massive spatially referenced data. Due in part to the dynamic …
Semantic ontology enabled modeling, retrieval and inference for incomplete mobile trajectory data
M Tao - Future Generation Computer Systems, 2023 - Elsevier
Prevalence of vehicular terminals enables more and more mobile trajectory data to be
collected. To promote the intelligent urban planning, traffic management and service in …
collected. To promote the intelligent urban planning, traffic management and service in …