Using Data Mining to Analyze the Traffic Data of Intersection

Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The historical traffic data is in fact capable of proving abundant of information that can aid in the development of improved current traffic control. However, many bad observations are hidden in databases with various faces. Data mining tools such as the k-means clustering approach are the keys to explore the information in the traffic data. This paper uses the k-means clustering approach to identify time-of-day (TOD) break points based on the historical data to support the design of signal timing plan. A case study using an intersection corridor was conducted to demonstrate the method.