Recent advances in sensor and mobile devices have enabled an unprecedented increase
in the availability and collection of urban trajectory data, thus increasing the demand for …

Due to the continued digitization of industrial and societal processes, including the
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …

Origin-destination (OD) matrices are used widely in transportation and logistics to record the
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …

In this work, we propose a robust road network representation learning framework called
Toast, which comes to be a cornerstone to boost the performance of numerous demanding …

Innovations in transportation, such as mobility-on-demand services and autonomous driving,
call for high-resolution routing that relies on an accurate representation of travel time …

Movement paths are used widely in intelligent transportation and smart city applications. To
serve such applications, path representation learning aims to provide compact …

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 …

Data are increasingly available that enable detailed capture of travel costs associated with
the movements of vehicles in road networks, notably travel time, and greenhouse gas …

The proliferation of trajectory data has facilitated various applications in urban spaces, such
as travel time estimation, traffic monitoring, and flow prediction. These applications require a …

Vehicle routing is an important service that is used by both private individuals and
commercial enterprises. Drivers may have different contexts that are characterized by …