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Data mining has attracted a great deal of attention not only the information industry and in society but also the commuters, electronic routing and players as a whole in recent years, due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. In this research, we develop a class of five novel and efficient methods for mining frequent trajectories from huge trajectory databases. Initial approach is by modifying the Apriori algorithm for finding frequent trajectory patterns. Secondly the frequent trajectory patterns are obtained by modifying the frequent pattern tree algorithm. Thirdly the longest frequent trajectories are obtained by modifying the association mining approaches. Fourth approach is that frequent trajectories of moving objects was found by a variable block box method. Fifth one is for finding the frequent trajectories of dynamic objects by clustering and sequential pattern mining approach.