An efficient technique for event location identification using multiple sources of urban data

Abstract

The proliferation of smart technologies has produced significant changes in the way people interact in a city. Smart traffic monitoring systems allow citizens and city operators to acquire a real-time view of the city traffic state. Furthermore, alternative means of transport, such as bike sharing systems, have enjoyed tremendous success in many major cities around the world today and provide real-time information regarding the mobility of the users. Such sources of urban data may act as human mobility sensors. Detecting the location and extent of large events in urban environments is a challenging problem. Previous work focuses mainly on identifying traffic flows and extract possible event sources. However, these solutions lack the ability to capture large areas of events, as they rely only on single-source data to identify user mobility or focus on identifying single locations rather than areas. In this paper we model the behavior of two different real-time data sources and we illustrate how they may be combined to acquire the area affected from a social event. We propose "fEEL" (Efficient Event Location identification), a novel algorithm to identify affected areas from social events using multiple heterogeneous sources of urban data. Our experimental evaluations show that fEEL is efficient and practical.