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Publication Detail
GEOTRAVELDIARY: Towards Online Automatic Travel Behaviour Detection
  • Publication Type:
    Conference
  • Authors:
    Bolbol ASZ, Cheng T, Paracha A
  • Place of publication:
    Italy
  • Status:
    Published
  • Name of conference:
    WebMGS: 1st International Workshop on Pervasive Web Mapping, Geoprocessing and Services
  • Conference place:
    Politecnico di Milano, Como, Italy
  • Conference start date:
    26/08/2010
  • Conference finish date:
    27/08/2010
  • Keywords:
    Transport, Spatiotemporal, GIS, Online, GPS, User-generated, Interface, Visualization
Abstract
Travel diaries are considered to be a very important source of information that benefits major applications such as travel behaviour analysis. They have also been proven to be a burden on users to maintain and recall exact details, as well as being slow, expensive and time consuming. However, the rise of Geoweb 2.0, crowd sourcing and user-generated content is changing the way data is collected and shared. Nowadays, travellers could record their tracks and are able to upload them directly into the web to see them and even share them. The aim of this paper is to develop an online user-generated travel diary system that records users’ trails. The online server-side application allows the users to visualize their trails and manually edit, move or remove points from their tracks on a map-based interface. Such a map-based interface enhances the retrieval of travel information from the users’ long-term memory. The interface also allows them to add such information as metadata to different parts of their trip, such as the purpose of the trip and the transport mode. The application also overcomes travel diary disadvantages by being faster, cheaper and by providing users with incentives provoking higher response rates. This in turn will compile a large database of travel information within a large city (such as London in this case). Therefore, this will provide a better understanding of travel behaviour, and hence providing a platform for developing a realistic algorithm for learning travel metadata and hence deducing it. The application has just been launched at the time of publishing this work, and further work involves testing the application on a large scale and adding more functionality.
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