Taxi trajectory and social media data management platform for tourist behavior | |
| Author | Pattama Krataithong |
| Call Number | AIT Diss no.IM-23-02 |
| Subject(s) | City traffic--Thailand--Data processing Social media--Thailand--Data processing Tourism--Mathematical models Tourism--Information technology Application software--Development Ontology |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | The development of mobility technology makes it possible to collect real-time data about tourists in a given location, including their geographical and temporal behavior. Movement data has become an essential alternative data source used in tourism studies. Taxis are an im portant mode of transportation employed by tourists visiting a new city. However, the main challenges of using taxi GPS data in the tourism domain are a lack of semantic information regarding trip purpose and user profile that could be used in an in-depth analysis of tourist behavior. This dissertation proposes TOURISTA data platform to manage and process heterogeneous data, including taxi data, social media data, and place data for tourist behavior analysis. We propose a data pipeline that can be scaled in order to process a significant amount of data regarding taxi trajectory and social media, with two objectives. The first objective is to ex tract the tourist trajectory data from the raw GPS data. This study proposes the TOURISTA model based on a rule-based and probabilistic model to infer the purpose of tourist trajecto ries based on activity and expenditure, considering origin-destination locations. We enhance an existing probabilistic model by building the model using a variety of data sources, includ ing taxi trajectory data, social media data, and place data. The second objective is to extract tourist activities/points of interests (POIs) from geo-tagged Twitter data. We examine ac tual tourist activity from social media data to build an activity popularity model, integrate it with trip and place information, and infer tourist trips using the probabilistic model. For the experiment, we investigated the TOURISTA model by comparing the activity proportions of three baseline methods with results of the Tourism Authority of Thailand’s (TAT) tourist behavior survey for five activities: FoodAndDrink, Spa, Nightlife, Religious/cultural, and Leisure. The results of the proposed method closely match the survey data for several activity categories. We applied data analysis techniques to a case study during the Songkran Festival in Bangkok to reveal tourists’ travel characteristics and activities, tourist movement behavior, and pop ular tourist destinations. The analysis results show that our study is useful and helps under stand tourist flow and the high density of tourist locations at different times, which is crucial for planning or marketing tourism campaigns targeted at specific tourist groups. Govern ment officials and tourism businesses can use this information to better plan or market their tourism campaigns. |
| Year | 2023 |
| Type | Dissertation |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Huynh, Trung Luong;Marut Buranarach; |
| Scholarship Donor(s) | National Science and Technology Development Agency (NSTDA);AIT Fellowship;Royal Thai Government; |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2023 |