Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Origin-destination (OD) flow modeling is an extensively researched subject across multiple
disciplines, such as the investigation of travel demand in transportation and spatial …

Urban hotspot forecasting is one of the most important tasks for resource scheduling and
security in future smart cities. Most previous works employed fixed neural architectures …

Traffic flow forecasting on a large-scale sensor network is of great practical significance for
policy decision-making, urban management, and transport planning. Recently, several …

MAUP (modifiable areal unit problem) is a fundamental problem for spatial data
management and analysis. As an instantiation of MAUP in online transportation platforms …

Origin-destination (OD) flow contains population mobility information between every two
regions in the city, which is of great value in urban planning and transportation …

Spatiotemporal crowd flow prediction is one of the key technologies in smart cities.
Currently, there are two major pain points that plague related research and practitioners …

Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-
based applications like ride-sharing. However, existing ST models often require region …

Origin-destination demand prediction is a critical task in the field of intelligent transportation
systems. However, accurately modeling the complex spatial-temporal dependencies …

Contextual features are important data sources for building citywide crowd mobility
prediction models. However, the difficulty of applying context lies in the unknown …