Analyzing travel mobility patterns in city destinations: Implications for destination design |
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| Affiliation: | 1. Smart Tourism Education Platform, College of Hotel and Tourism Management, Kyung Hee University, Seoul, Republic of Korea;2. Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China;3. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China;4. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China;5. Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China;6. Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Switzerland;1. Copenhagen Business School, Department of Marketing, Solbjerg Plads 3, 2000, Frederiksberg, Denmark;2. JCU Singapore Business School, James Cook University, 149 Sims Drive, Singapore, 387380, Singapore;3. Department of Hospitality and Tourism Management, Isenberg School of Management, University of Massachusetts, Amherst, 121 Presidents Dr, Amherst, MA, 01003, USA;4. School of Travel Industry Management, Shidler College of Business, University of Hawaii at Mānoa, 2560 Campus Road, Honolulu, HI, 96822, USA;1. School of Business, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China;2. Faculty of Hospitality and Tourism Management, Macau University of Science and Technology, Taipa, Macau, 999078, China;3. School of Tourism and Urban-rural Planning, Zhejiang Gongshang University, Hangzhou, Zhejiang, 310018, China;1. School of Economics, University of Nottingham, Ningbo, China;2. Applied Economics Department, Universidad de las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain;1. Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Tourism and Development Research Group, Department of Economics, Management, Industrial Engineering and Tourism, University of Aveiro, 3810-193 Aveiro/Portugal;2. Mardin Artuklu University, Faculty of Tourism, 47080 Artuklu, Mardin, Turkey;3. Zangador Research Institute, 9010 Varna, Bulgaria;4. Varna University of Management,13A Oborishte Str., 9000 Varna, Bulgaria |
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| Abstract: | Understanding the features of travel activities is important in elaborating travel behaviors and segmenting travelers based on the similarity of activity patterns. This research applying mobile big data analytics suggests a novel method to classify travelers by considering the sequences of travel activity with individuals' trajectories. The result revealed five distinct travel types visiting city destinations and demonstrated dynamic travel flow among different mobility types. Recognizing that different types of travel patterns present important information in understanding destinations’ roles (or functions), this study attempts to characterize the functionality dynamics of city destinations based on travel activity types. As a result, the findings of this research provide insights into the demand-driven construct (or flow-based) of destination planning, which is the foundation of smart destination design. In addition, important methodological and practical implications that could be useful for city destination planners/designers are suggested. |
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| Keywords: | Tourist Mobility Travel activity type Place design Mobile phone data Destination function |
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