Identifying Tourists from Public Transport Commuters

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Identifying Tourists from Public Transport Commuters
Title:
Identifying Tourists from Public Transport Commuters
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KDD '14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
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Publication Date:
27 August 2014
Citation:
Abstract:
Tourism industry has become a key economic driver for Singapore. Understanding the behaviors of tourists is very important for the government and private sectors, e.g., restaurants, hotels and advertising companies, to improve their existing services or create new business opportunities. In this joint work with Singapore's Land Transport Authority (LTA), we innovatively apply machine learning techniques to identity the tourists among public commuters using the public transportation data provided by LTA. On successful identification, the travelling patterns of tourists are then revealed and thus allow further analyses to be carried out such as on their favorite destinations, region of stay, etc. Technically, we model the tourists identification as a classification problem, and design an iterative learning algorithm to perform inference with limited prior knowledge and labeled data. We show the superiority of our algorithm with performance evaluation and comparison with other state-of-the-art learning algorithms. Further, we build an interactive web-based system for answering queries regarding the moving patterns of the tourists, which can be used by stakeholders to gain insight into tourists' travelling behaviors in Singapore.
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PublisherCopyrights
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Description:
© ACM 2014. 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 KDD '14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, http://dx.doi.org/10.1145/2623330.2623352 .
ISBN:
978-1-4503-2956-9
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