Widespread traffic congestion significantly impacts the quality of life, posing several
problems and challenges. To reduce traffic congestion, it is necessary to have accurate …

This paper presents an integral lattice hydrodynamic model to examine the impact of driver's
anticipation and driving prediction with density deviation of leading vehicle under passing …

In real-world driving scenarios, numerous factors such as driver behavior, road conditions,
weather conditions and vehicle capabilities contribute to deviations among the driver's …

Objectives: In order to control and manage the traffic flow in contexts of congestion, real-time
highway traffic flow models play a significant role in intelligent transportation system. The …

In the era of big data, traffic prediction, along with deep learning, has gained attention in
previous years. Urban planners have long been concerned by the constant challenges of …

As the urbanization rate in China has continued to increase, the highway congestion
problem has become more severe, significantly reducing the efficiency of traffic operations …

Traffic flow modeling has a pivotal role within Intelligent Transportation Systems (ITSs),
holding vital importance in alleviating traffic congestion and decreasing carbon emissions …

Traffic speed prediction has implications for urban planning, congestion reduction, and
intelligent control systems. To maintain a uniform traffic speed and to avoid issues related to …

The state changes of short-term traffic speeds in rapidly changing urban environments
depend not only on historical data and their own patterns, but also closely relate to other …

This study introduces the MSTA-GNet (Multi-Scale Spatiotemporal Attention Graph Network),
a novel deep learning model which integrates spatiotemporal self-attention mechanisms to …