When intelligent transportation systems sensing meets edge computing: Vision and challenges
The widespread use of mobile devices and sensors has motivated data-driven applications
that can leverage the power of big data to benefit many aspects of our daily life, such as …
that can leverage the power of big data to benefit many aspects of our daily life, such as …
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 …
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 …
Missing data repairs for traffic flow with self-attention generative adversarial imputation net
With the rapid development of sensor technologies, time series data collected by multiple
and spatially distributed sensors have been widely used in different research fields …
and spatially distributed sensors have been widely used in different research fields …
Traffic dataset and dynamic routing algorithm in traffic simulation
The purpose of this research is to create a simulated environment for teaching algorithms,
big data processing, and machine learning. The environment is similar to Google Maps, with …
big data processing, and machine learning. The environment is similar to Google Maps, with …
A spatio-temporal decomposition based deep neural network for time series forecasting
Spatio-temporal problems arise in a broad range of applications, such as climate science
and transportation systems. These problems are challenging because of unique spatial …
and transportation systems. These problems are challenging because of unique spatial …
An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach
Air pollution forecasting is a significant step for air quality pollution management to mitigate
pollution's negative impact on the environment and people's health. The data-driven …
pollution's negative impact on the environment and people's health. The data-driven …
Dynamic spatiotemporal graph convolutional neural networks for traffic data imputation with complex missing patterns
Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Despite extensive research regarding traffic data imputation, there …
transportation systems. Despite extensive research regarding traffic data imputation, there …
[HTML][HTML] Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images
Sufficient high-quality traffic data are a crucial component of various Intelligent
Transportation System (ITS) applications and research related to congestion prediction …
Transportation System (ITS) applications and research related to congestion prediction …
Toward Resilient Electric Vehicle Charging Monitoring Systems: Curriculum Guided Multi-Feature Fusion Transformer
With the booming adoption of Electric Vehicles (EVs) globally, the need for reliable and
resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in …
resilient EV Charging Monitoring (EVCM) systems has become crucial. A major challenge in …