Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Short-term origin–destination (OD) flow prediction in urban rail transit (URT) plays a crucial
role in smart and real-time URT operation and management. Different from other short-term …

Region-level passenger demand prediction plays an important role in the coordination of
travel demand and supply in the urban public transportation system. The complex urban …

The current revolutions of automation, electrification, and sharing are reshaping the way we
travel, with broad implications for future mobility management. While much uncertainty …

This study provides a comprehensive review of the significant elements in sustainable
transportation systems with shared mobility. The main subsets of shared mobility includes …

Ride-sourcing services are increasingly popular because of their ability to accommodate on-
demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand …