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Location Based Social Network Case Study 1

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In the recent times, there is a rapid growth of location based social networking applications such as Twitter, Yelp and Foursquare, which have an increasing number of social media users. Nowadays, fundamental activity of the modern society is to measure the movement of the people(Lun et al.). The study of mobility behavior and travel activity analysis can be done by developing Origin-Destination (OD) models and to investigate their performance using location based social network data which is capable with the potential to provide traffic design with higher spatial and temporary resolution at lower cost than traditional methods of survey(Kheiri, Karimipour, and Forghani 2015). This data was applied to analyze the trip attraction for the…show more content…
Therefore, they practically investigated how gender is important in the travel and activity design of active local Foursquare users in New York City(Yeran and Ming 2015). The results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. By contrasting at the spatial dispersion of Twitter based data in weekdays with other more generally used original source of data to authenticate social network based data, four step planning model was used to obtain OD pairs to understand zoning level at aggregate level and to compare twitter based trips with travel-based travel demand model trips and to develop a conversion method from OD pattern from tweets from regression models(Lee, Gao, and Goulias 2015). Dissimilar to other studies in this field, there is a challenge in the range to make travel behavior modeling to get in-home activities of the individual. Twitter clients frequently give data about their everyday exercises which can be used to obtain information like time, location and purpose behind their distinctive exercises, particularly if it is connected to ground truth data. Utilizing Twitter information for developing tour formation model that can notably added to models that are developed using household travel data(Maghrebi et al. 2015). (Pianese
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