Online event clustering in temporal dimension

This work is motivated by a real-life application that exploits sensor data available from traffic light control systems currently deployed in many cities around the world. Each sensor consists of an induction loop that generates a stream of events triggered whenever a metallic object e.g. car, bus, or a bicycle, is detected above the sensor. Because of the red phase of traffic lights objects are usually divided into groups that move together. Detecting these groups of objects as long as they pass through the sensor is useful for estimating the status of the toad networks such as car queue length or detecting traffic anomalies. In this work, given a data stream that contains observations of an event, e.g. detection of a moving object, together with the timestamps indicating when the events happen, we study the problem that clusters the events together in real-time based on the proximity of the event's occurrence time. We propose an efficient real-time algorithm that scales up to the large data streams extracted from thousands of sensors in the city of London. Moreover, our algorithm is better than the baseline algorithms in terms of clustering accuracy. We demonstrate motivations of the work by showing a real-life use-case in which clustering results are used for estimating the car queue lengths on the road and detecting traffic anomalies.