2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (2018)
Jan 15, 2018 to Jan 17, 2018
Large scale of locational data generated by mobile devices present an opportunity to change the structure of traditional research in tourism. However, tourist-focusing mobility patterns haven't been explored enough, which are tremendously useful for optimizing tourism resource allocation. To fulfill the need, we design a new analysis framework for understanding travel patterns of tourists by using the massive anonymous CDRs. The analysis framework consists of three layers that are data layer, algorithm layer and application layer. A new region-activity-time (RAT) pattern that captures the multi-dimensional mobility information of tourists is defined in the algorithm layer. The application layer shows the usability of algorithms in discovering hot regions and popular travel patterns, analyzing tourism seasonality effect as well as tourists' lodging preference. Our framework provides tourism experts with valuable information of tourist mobility and helps enhance tourism management.
mobile radio, mobility management (mobile radio), resource allocation, travel industry
Q. Chen, Z. Hu, H. Su, X. Tang and K. Yu, "Understanding Travel Patterns of Tourists from Mobile Phone Data: A Case Study in Hainan," 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018, pp. 45-51.