Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …

The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …

The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …

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 …

Urbanization's rapid progress has modernized many people's lives but also engendered big
issues, such as traffic congestion, energy consumption, and pollution. Urban computing …

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 …

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road
surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a …

This paper presents a Cloud-based system computing customized and practically fast
driving routes for an end user using (historical and real-time) traffic conditions and driver …

Data-driven intelligent transportation systems utilize data resources generated within
intelligent systems to improve the performance of transportation systems and provide …

Wide deployment of global positioning system (GPS) sensors has generated a large amount
of data with numerous applications in transportation research. Due to the observation error …