BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Learning Significant Locations and Predicting User Movement with GPS
ISWC '02 Proceedings of the 6th IEEE International Symposium on Wearable Computers
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
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Telematics systems that integrate wireless communications with sensor-based monitoring and location-aware applications have been widely deployed for mobile asset tracking and condition monitoring. In asset tracking field, exploring the data that relate to asset behaviors is critical to understand asset utilization, efficiency, distribution, operation, and many other important aspects in the supply chain. Prior work on analyzing GPS-based patterns has mainly been performed on time-based datasets. In this paper, we describe a scalable clustering algorithm to discover frequently repeated trips from large-scale, event-based telematics datasets collected via a satellite-based tracking system. We first transform GPS traces into a list of trips. Then we present a grid-based hierarchical clustering algorithm to discover frequent spatial patterns among all trips. We evaluate the effectiveness of the proposed algorithm against a large-scale, real-world dataset collected from tracking over a hundred of thousand assets and prove its feasibility. Through these experimental results, we show that the proposed algorithm significantly reduces the computational time needed for clustering as opposed to the traditional hierarchical clustering based on pairwise comparison.