Sensor Data Analysis by means of Clustering

Traffic jams are a big problem of the society nowadays, especially incase of urban traffic. To solve the problem of traffic congestion and air pollution, the intelligent transportation systems (ITS) should be developed and integrated into transport infrastructure. The core element of such ITS is a reliableand accurate forecasting model to predict traffic flow in a short-term period.Lots of historical traffic data can be used as input of the model, in particulardaily traffic profiles. Different dates have different traffic flow patterns, andmodern prediction models should take into account such temporal variations.This paper investigates the historical traffic flow data, which was obtained fromstationary road sensors. The main goal of this research is to obtain more insightinto urban traffic by analyzing between day and between month variations intraffic volumes. By means of k-means clustering procedure, we divide dailytraffic profiles of each sensor into several groups and examine the obtainedclusters with respect to day type (day-of-week, preholiday and holiday) andseasonal variations. For most road sensors, there is a significant difference between the daily traffic flow profiles for working and non-working days. Thecentroid of the first one demonstrates two prominent flow peaks (morning andevening), whilst the second one represents only one day peak with slow growthand slow decrease of flow rate. We discovered seasonal variation for some roadsensors, but it is less pronounced than the variations between weekdays.