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Utilising Location Based Social Media in Travel Survey Methods: bringing Twitter data into the play

Published: 03 November 2015 Publication History

Abstract

A growing body of literature has been devoted to harnessing the crowdsourcing power of social media by extracting knowledge from the huge amounts of information available online. This paper discusses how social media data can be used indirectly and with minimal cost to extract travel attributes such as trip purpose and activity location. As a result, the capacity of Twitter data in complementing other sources of transport related data such as household travel surveys or traffic count data is examined. Further, a detailed discussion is provided on how short term travellers, such as tourists, can be identified using Twitter data and how their travel pattern can be analysed. Having appropriate information about tourists/visitors -- such as the places they visit, their origin and the pattern of their movements at their destination -- is of great importance to urban planners. The available profile information of users and self-reported geo-location data on Twitter are used to identify tourists visiting Sydney as well as also those Sydney residents who made a trip outside Sydney. The presented data and analysis enable us to understand and track tourists' movements in cities for better urban planning. The results of this paper open up avenues for travel demand modellers to explore the possibility of using big data (in this case Twitter data) to model short distance (day-to-day or activity based) and long distance (vacation) trips.

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cover image ACM Conferences
LBSN'15: Proceedings of the 8th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
November 2015
47 pages
ISBN:9781450339759
DOI:10.1145/2830657
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 03 November 2015

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Author Tags

  1. Location based social media
  2. Social media
  3. Topic modelling
  4. Travel demand analysis

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