Exploring spatio-temporal patterns in traffic congestion data

An efficient infrastructure is essential for economic development. However, economic growth has been closely connected to the increasing road transport. This increases traffic congestions significantly, and road network gets near or at its capacity limits. Hence, congestion has become a central problem in modern cities. Existing traffic information systems do rarely provide optimized information for both users and operators to make quick decisions on alternative routes, and to optimize the capacity of the road network. Therefore, development of an effective and efficient system that will enhance information on traffic congestion and road operation for an intelligent decision-making is essential. Over the last decades, the research interest in traffic congestion has been increasing, but more detailed studies are needed to alleviate existing traffic issues. In this research, we use a systematic approach to investigate the spatio-temporal distribution of traffic congestion patterns on route segments at different temporal granularities to reveal the known problems and discover the unknown. Based on the high-frequent data collected in city of Aalborg (Denmark), through GNSS receivers for 425 cars in two years, an intensive exploration and analysis of complex transport data is considered. Based on the information such as location, time, segment length and road categories, the average time and speed required to cover each route segment will be calculated. The used visualization methods should lead to a better understanding of the behavior of movement patterns and the factors that are influencing the traffic phenomena.