FTT: A System for Finding and Tracking Tourists in Public Transport Services

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FTT: A System for Finding and Tracking Tourists in Public Transport Services
Title:
FTT: A System for Finding and Tracking Tourists in Public Transport Services
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SIGMOD'15 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
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Publication Date:
27 May 2015
Citation:
Huayu Wu, Jo-Anne Tan, Wee Siong Ng, Mingqiang Xue, and Wei Chen. 2015. FTT: A System for Finding and Tracking Tourists in Public Transport Services. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD '15). ACM, New York, NY, USA, 1093-1098. DOI=http://dx.doi.org.ejproxy.a-star.edu.sg/10.1145/2723372.2735367
Abstract:
The tourism industry is a key economic driver for many cities. To understand tourists' traveling patterns can help both public and private relevant sectors design and improve their services to serve tourists better and get additional values from it. The existing approaches to discover tourists' traveling pattern focus on small sets of known tourists extracted from social media or other channels. The accuracy of the mining result cannot be guaranteed due to the small and bias set of samples. In this paper, we present our system FTT (Finding and Tracking Tourists) to identify tourists from public transport commuters in a city, and to further track their movements from one place to another. Our target is a large set of tourists and their trajectories extracted from public transport riding records, which more accurately represent the movements of general tourists. In particular, we design an iterative learning algorithm to find the tourists among public transport commuters, and provide interface to answer user queries on tourists' traveling patterns. The result will be visualized on top of a city map.
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Description:
© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, http://dx.doi.org/10.1145/10.1145/2723372.2735367
ISBN:
978-1-4503-2758-9
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