With the prevalence of positioning techniques, a prodigious number of spatio-temporal data
is generated constantly. To effectively support sophisticated urban applications, eg, location …

This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph
Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle …

With the advances of web-of-things, human mobility, eg, GPS trajectories of vehicles,
sharing bikes, and mobile devices, reflects people's travel patterns and preferences, which …

With the development of positioning technology, a large number of trajectories have been
generated, which are very useful for many urban applications. However, it is challenging to …

Recent advances in location-acquisition techniques have generated massive spatial
trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such …

The rapid development of e-commerce requires efficient and reliable logistics services.
Nowadays, couriers are still the main solution to address the" last mile" problem in logistics …

The advent of connected vehicles (CVs) starts a new era in the field of pavement evaluation.
With CVs on roads, the wide fleet of vehicles will constantly transmit pavement data to …

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although
considerable efforts have been made to solve this problem, most of them decompose a …

Transformers have been an efficient alternative to recurrent neural networks in many
sequential learning tasks. When adapting transformers to modeling trajectories, we …

Maps services are ubiquitous in widely used applications including navigation systems, ride
sharing, and items/food delivery. Though there are plenty of efforts to support such services …