Detecting Congestion Patterns in Spatio Temporal Traffic Data Using Frequent Pattern Mining

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis and it is a process of extracting valuable and invaluable information from the large data base. Congestion on road is the condition in which it is characterized as slow speed and long travel time. The detection of unusual traffic patterns is an important research problem in the data mining. In this research, the detection of unusual traffic patterns based on spatio-temporal traffic data is by constructing causal congested tree and then to find the frequent sub tree, FP-Growth algorithm is used. Frequent substructures of these causality trees reveal not only recurring interactions among spatial-temporal congestions, but potential bottlenecks or flaws in the design of existing traffic networks. The FP-Growth algorithm is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree.