Deriving Traffic Flow Patterns from Historical Data

The development and decreased cost of technology and communications have brought about a huge increase in the availability of traffic data. With every passing day, traffic management centers must deal with an increased amount of detailed data. Once the real time use of these data is complete, they must be stored for long periods of time. In this long-term context, the vast amount of raw data is meaningless, which is a clear example of data asphyxiation. Traffic management centers must aggregate and synthesize the data to extract the maximum information from them. Pattern classification is a way to deal with this issue. Traditionally, traffic demand patterns have been easily constructed using ad hoc methods, where the experience and judgment of the analyst are their main attribute. These procedures lack the required rigor to support current needs in terms of planning and operational management. This paper proposes a quantitative method to systematically derive traffic demand patterns from historical data. The method is based on the cluster analysis technique and allows for the inclusion of preexisting knowledge, which eases the interpretation and practical use of the results. The proposed pattern classification procedure is applied to 5 years of hourly traffic volumes on a Spanish highway. The obtained results prove the validity and utility of the method in accurately summarizing the seasonal and daily characteristics of traffic demand.