Machine learning is a promising technology for analyzing diverse types of big data. The
Internet of Things era will feature the collection of real-world information linked to time and …

We present a novel approach for real-time and proactive navigation in crowded
environments such as event spaces and urban areas where many people are moving to …

Studying human movement citywide is important for understanding mobility and
transportation patterns. Rather than investigating the trajectories of individuals, we employ …

Analyzing structures of crowd mobility at city level is a challenging task due to the complex
crowd mobility and dynamic changes generated by the social activities over time. These …

The increasing availability of tremendous amounts of data generated by people, vehicles,
and things have provided unprecedented opportunities for understanding human behavior …

Studying the movements of crowds is important for understanding and predicting the
behavior of large groups of people. When analyzing crowds, one is often interested in the …

Data generators have been heavily used in creating massive trajectory datasets to address
common challenges of real-world datasets, including privacy, cost of data collection, and …

Metropolitan cities are facing many socio-economic problems (eg, frequent traffic
congestion, unexpected emergency events, and even human-made disasters) related to …

Over the last decade, the infrastructure supporting the smart city has lived together with and
was surpassed by the rise of social media. The tremendous growth of both mobile devices …

In recent years, the development of information and communication technologies (ICTs) has
made it technically and economically feasible to collect huge amounts of spatiotemporal …