Incremental frequent route based trajectory prediction
2013 (English)In: IWCTS 2013 - 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, Association for Computing Machinery (ACM), 2013, 49-54 p.Conference paper (Refereed)
Recent technological trends enable modern traffic prediction and management systems in which the analysis and prediction of movements of objects is essential. To this extent the present paper proposes IncCCFR - a novel, incremental approach for managing, mining, and predicting the incrementally evolving trajectories of moving objects. In addition to reduced mining and storage costs, a key advantage of the incremental approach is its ability to combine multiple temporally relevant mining results from the past to capture temporal and periodic regularities in movement. The approach and its variants are empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2013. 49-54 p.
Frequent Routes, Incremental Mining, Spatio-Temporal Data Mining, Time Inhomogeneous Trajectory Prediction
Computer Science Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-143820DOI: 10.1145/2533828.2533840ScopusID: 2-s2.0-84892554878ISBN: 978-1-4503-2527-1OAI: oai:DiVA.org:kth-143820DiVA: diva2:712266
6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2013; Orlando, FL; United States; 5 November 2013 through 5 November 2013
QC 201404142014-04-142014-03-312014-04-14Bibliographically approved