Urban spatiotemporal flow prediction is of great importance to traffic management, land use,
public safety. This prediction task is affected by several complex and dynamic factors, such …

Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays …

Incident detection is fundamental for freeway management to reduce non-recurrent
congestions and secondary incidents. Recently, machine learning technologies have made …

Link prediction is a fundamental and key field in complex network research, and some
scholars have conducted various studies in this field. However, most of the existing link …

A fast architecture for real-time (ie, minute-based) training of a traffic predictor is studied,
based on the so-called broad learning system (BLS) paradigm. The study uses various traffic …

Understanding the travel flux between urban blocks is fundamental for traffic demand
prediction, urban area planning and urban traffic management. However, the uncertainty of …

Emerging trends in future Intelligent Transportation System allow vehicles to have on‐board
sensors and digital computers, which might help in real‐time information exchange among …

A more complete taxi pick‐ups prediction going beyond the conditional expectation would
be more beneficial to allocate taxis effectively. Prior works have predicted the conditional …

With the development of civil aviation in China, airspace congestion has become more and
more serious and has gradually spread from airport terminal areas to en route networks …

Origin-destination (OD) matrices provide significant information to optimize the operation of
Public Transportation System and design its evolution. The survey-based approaches to …