The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …

Abstract Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of
the multiscale transportation system. Existing methods aim to reconstruct STTD using low …

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

High-accuracy long-coverage vehicle trajectory data can benefit the investigations of various
traffic phenomena. However, existing datasets frequently contain broken trajectories due to …

With the acceleration of urbanization, urban traffic congestion is becoming more and more
serious, in which the timing of signal lights for regional traffic optimization is particularly …

Assessing traffic states accurately is challenging due to the complex, high-dimensional, and
nonlinear nature of traffic systems. This study introduces the innovative High-Order …

Urban rail transit (URT) systems face the challenge of sharp increases in passenger flow
during large-scale events. However, existing research mainly focuses on normal short-term …

The data-driven approach in intelligent traffic systems has achieved successive
breakthroughs, thanks to the ever-increasing volume of traffic data. Nonetheless, in practical …

Network‐wide short‐term passenger flow prediction is critical for the operation and
management of metro systems. However, it is challenging due to the inherent non …

The multi-mode integrated railway system, anchored by the high-speed railway, caters to the
diverse travel requirements both within and between cities, offering safe, comfortable …