On Event Detection from Spatial Time Series for Urban Traffic Applications

Since the last decades the availability and granularity of location-based data has been rapidly growing. Besides the proliferation of smartphones and location-based social networks, also crowdsourcing and voluntary geographic data led to highly granular mobility data, maps and street networks. In result, location-aware, smart environments are created. The trend for personal self-optimization and monitoring named by the term ‘quantified self’ will speed-up this ongoing process. The citizens in conjunction with their surrounding smart infrastructure turn into ‘living sensors’ that monitor all aspects of urban living (traffic load, noise, energy consumption, safety and many others). The “Big Data”-based intelligent environments and smart cities require algorithms that process these massive amounts of spatio-temporal data. This article provides a survey on event processing in spatio-temporal data streams with a special focus on urban traffic.