What is it about?
Big data from social media provide information about the mobility and activity patterns of travelers. We reviewed and published evidence about the potential use of social media data for suggesting more efficient locations for joint activities. The trials were conducted using social media data from London with the objective to find optimal locations for joint leisure activities. This is one of our first studies that tries to improve the selection of joint activity locations.
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Why is it important?
Individuals decide where and when to meet (i.e., at which restaurant) through an informal negotiation process that does not consider all available options. For instance, travelers might need to commute long distances to the location of their joint activity, whereas there was another equally good option closer to their origins. We found that by using historical data from users mobility and activity patters, we can develop user preference profiles and automate the decision process of where and when to meet; thus, saving a lot of commuting time and reducing the traveling costs.
Perspectives
Lots of people spend many hours and commute long distances to participate in joint activities that do not have fixed locations (i.e., restaurants, cafes and so on). There is no research as to whether this process can be improved. For suggesting valuable improvements, the mobility and activity patterns of users should be derived by exploring past data. Unfortunately, past data of the mobility of people is not available and geo-tagged data from social media only covers a small fraction of the population (in the range of 1-4%). We need more research to systematize this process and new ways to enable individuals share their mobility information without infringing their privacy rights. That will facilitate several applications such as improving the efficiency of commuting and selecting locations for joint activities.
Dr. Konstantinos Gkiotsalitis
University of Twente
Read the Original
This page is a summary of: A utility-maximization model for retrieving users’ willingness to travel for participating in activities from big-data, Transportation Research Part C Emerging Technologies, September 2015, Elsevier,
DOI: 10.1016/j.trc.2014.12.006.
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