Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data |
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| Institution: | 1. Department of Transport and Planning, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands;2. Department of the Built Environment, Section of Urban Systems and Real Estate, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands;3. Department of Air Transportation Management, Nanjing University of Aeronautics and Astronautics, China;1. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States;2. Department of Civil and Environmental Engineering, Northeastern University, Boston, MA 02115, United States;3. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 02139, United States;1. Department of Engineering and Computer Science, Tarleton State University, Stephenville, TX 76401, United States;2. Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, United States;1. NSFC-DFG Sino-German Cooperation Group on Urbanization and Locality (UAL); College of Architecture and Landscape Architecture, Peking University, Beijing, 100871, PR China;2. College of Urban and Environmental Sciences, Peking University, Beijing, 100871, PR China;1. School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan 410000, China;2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China;3. Shenzhen Urban Transport Planning Center, Shenzhen, Guangdong 518021, China;4. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;1. School of Politics and Social Development, Zaozhuang University, Zaozhuang, 277100, China;2. School of Economics and Management, Tongji University, Shanghai, 200092, China;3. Institute of Smart City and Electronic Governance, Shanghai, 200092, China;4. School of Economics, Shandong University of Technology, Zibo, 255000, China |
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| Abstract: | New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas. |
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| Keywords: | Mobile internet usage Mobile phone data Travel behaviour Mobility analysis Data fusion |
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