Trajectory Pattern Mining for Moving Objects in Road Networks

Pattern mining on paths and roads data has brought up valuable and effective achievements in different areas, such as: roads and urbanization, transportation, and even forecasting social services. The trajectory patterns of moving objects in road networks were examined to extract efficient patterns. Among the challenges in clustering techniques of pattern mining is to find consecutive patterns with low run time. To solve this problem, a clustering algorithm was proposed titled BFEs-Enhanced, which can be used to extract sequential and meaningful flock patterns with a short time interval. Simulation results showed that the proposed method, thanks to the use of efficient criteria for clustering, such as maximum distance and minimum number demonstrated an outstanding performance. In addition, a new criterion was added to the algorithm called minimum time period. Along with the distance criterion in BFE-Enhanced algorithm, the criterion of direction was also added to the algorithm to improve its accuracy. Therefore, the proposed algorithm is featured with a shorter time interval and higher number of meaningful and sequential patterns.