Predicting future urban health status is significant in terms of identifying urban diseases and
urban planning. Current studies have focused on using machine learning and deep learning …

The expansion of the Internet of Things has resulted in new creative solutions, such as smart
cities, that have made our lives more productive, convenient, and intelligent. The core of …

Traffic prediction is a popular research topic in the field of Intelligent Transportation System
(ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Compared with single-task learning, multi-tasks can obtain better classifiers by the
information provided by each task. In the process of multi-task data collection, we always …

Traffic flow/volume data are commonly used to calibrate and validate traffic simulation
models. However, these data are generally obtained from stationary sensors (eg loop …

Truck speed prediction plays a key role in truck transportation management. However, it is a
very challenging task since the truck traffic usually shows complex patterns. Most of the …

Existing text classification models based on graph convolutional networks usually update
node representations simply by fusing neighborhood information of different orders through …

Accurate traffic volume and speed prediction have a wide range of applications in
transportation. It can result in useful and timely information for both travellers and …

Forecasting traffic congestion in large-scale transportation networks is a challenging
problem of great importance in intelligent transportation systems. Most previous studies …