With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

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

Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …

Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as
subway/bus stations, is a practical application and of great significance for urban traffic …

Urban traffic passenger flows prediction is practically important to facilitate many real
applications including transportation management and public safety. Recently, deep …

Abstract Graph Convolutional Network (GCN) has been widely applied in transportation
demand prediction due to its excellent ability to capture non-Euclidean spatial dependence …

Taxi demand prediction has recently attracted increasing research interest due to its huge
potential application in large-scale intelligent transportation systems. However, most of the …

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 …

Predicting traffic flow on traffic networks is a very challenging task, due to the complicated
and dynamic spatial-temporal dependencies between different nodes on the network. The …

Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing
services. However, predicting passenger demand over multiple time horizons is generally …