Real-time taxi dispatching using Global Positioning Systems
Communications of the ACM - Wireless networking security
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Data Mining Using SAS Enterprise Miner (Wiley Series in Computational Statistics)
Data Mining Using SAS Enterprise Miner (Wiley Series in Computational Statistics)
A Telematics Service System Based on the Linux Cluster
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation
NCM '08 Proceedings of the 2008 Fourth International Conference on Networked Computing and Advanced Information Management - Volume 01
Association analysis of location tracking data for various telematics services
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
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This paper proposes an analysis framework which enables us to analyze the moving patterns of moving objects. To show the effectiveness of the framework, we applied the framework to analyze moving patterns of taxis based on the real-life location history data accumulated from the Taxi Telematics system developed in Jeju, Korea. The analysis aims at obtaining value-added information necessary to provide empty taxis with location recommendation services for the efficient operations of taxis. The proposed framework provides the flow chart which would have a quick look around the overall analysis process and help quickly deal with the same or similar analysis, while saving the temporal and economic costs. Data mining tool used in the framework is Enterprise Miner (E-Miner) in SAS which is one of the most widely used statistics packages and can effectively address huge amounts of log data. Especially, we perform the refined analysis by means of doing repeatedly the well-known k-means clustering method under various spatial or temporal conditions. The paper proposes the refined data mining process 1) extracting the interested dataset about meaningful information driven from the previous cluster results, 2) performing again the detailed clustering with the extracted dataset, and 3) finally extracting the value-added information such as the good pick-up spots or 4) returning the feedback. As a result, the spatiotemporal pattern analysis within the each refined clustering method makes it possible to recommend that the empty taxis go to the nearby cluster location with a high pick-up frequency statistically, resulting in the reduction of empty taxi ratio.