Abstract
Each traveler moves across the physical plane to perform activities. It is known that each trip connects two distinct activities. Travelers, during their trips, make various choices in order to decide mode, route and time of departure. These choices depend on factors that are either predetermined or emotional. Other factors such as existence of various events can also affect the choices of the travelers. During the last decade, information related to the factors mentioned above, are addressed through the social networks. The amount of information provided in the social media is important and crucial in addressing the way travelers move around. On the other hand, understanding and, more importantly, predicting activities is a crucial matter in order to predict traffic conditions as well as to provide improved trip advice to travelers.
The present paper studies the possibilities and capabilities exist in order to proceed to transport modelling techniques by deriving information from the social media status updates of the users. More specifically the study reviews methodologies and techniques that can collect information from the users’ status updates in order to estimate their preferences.
In the present study the development of a methodology which integrates the gathered information from the social media status updates with stated activities’ preferences is being investigated. The review takes into account the social computing paradigm where humans and machines collaborate to solve a social problem. Also, multiple data sources are examined in order more integrated results to be returned.
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Acknowledgement
The present paper presents the findings of the literature review and the proposed methodology conducted within the framework of My-TRAC Project (funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777640).
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Chalkiadakis, C., Iordanopoulos, P., Mitsakis, E., Chalkia, E. (2019). The Use of Social Computing in Travelers’ Activities Preference Analysis. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_37
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DOI: https://doi.org/10.1007/978-3-030-02305-8_37
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