An instant discovery method for companion vehicles based on incremental and parallel calculation

Abstract

In the field of intelligent transportation, the instant discovery of companions has become a research hotspot. This technique can be applied to traffic management and public security governance. This study provides an incremental and distributed approach for discovering traveling companion instantly and continuously based on a data stream of automatic number plate recognition(ANPR). First, a parallelized incremental mining algorithm is designed and implemented in Spark on the basis of traffic-monitoring streaming data. Second, an adjustable data structure DF-tree is proposed that considers the characteristics of companion vehicles with the original ANPR data stream changing dynamically. On the basis of the DF-tree, the system can discover companions without reconstructing the data tree. In addition, we introduce a time decay mechanism to satisfy the spatio-temporal constraints of companion vehicles discovery. Finally, we realize the real-time discovery of companion vehicles based on large-scale ANPR data. The proposed methods are evaluated with extensive experiments on real datasets. The experimental results show that our proposed DF-tree-based approach is faster than the existing methods for companion discovery and it can detect companion vehicles groups in real time.