Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic
efficiency. To address the challenge of the inherently complex and dynamic growth of the …

Accurate traffic flow prediction is critical for traffic management and route guidance, enabling
urban traffic to be free-flowing conditions and maximizing transport efficiency. In current …

As autonomous vehicles (AVs) advance from theory into practice, their safety and
operational impacts are being more closely studied. This study aims to contribute to the ever …

Graph neural networks (GNNs) have been extensively employed in traffic prediction tasks
due to their excellent capturing capabilities of spatial dependence. However, the majority of …

Secondary crashes occur within the spatial and temporal impact area of primary crashes,
resulting in traffic delays and safety problems. While most existing studies focus on the …

An application of convolutional neural network (CNN) technique for road surface defects
detection is presented in this paper. You only look ones (YOLO) algorithm showed its …

It is generally recognized that accurate and timely prediction of future traffic flow information
is one of the important conditions for improving the utilization rate of road networks and …

Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems
and urban planning. Nonetheless, missing data in traffic datasets is a common challenge …

Accurate production forecasting is an essential task and accompanies the entire process of
reservoir development. With the limitation of prediction principles and processes, the …

The extraction of spatial-temporal features is a crucial research in transportation studies, and
current studies typically use a unified temporal modeling mechanism and fixed spatial graph …