Analyzing travel mobility patterns in city destinations: Implications for destination design

Sangwon Park; Jinyan Zu; Yang Xu; Fan Zhang; Yu Liu; Jingyan Li

doi:10.1016/j.tourman.2022.104718

Analyzing travel mobility patterns in city destinations: Implications for destination design

Sangwon Park, Jinyan Zu, Yang Xu (Corresponding Author), Fan Zhang, Yu Liu, Jingyan Li

Research output: Journal article publication › Journal article › Academic research › peer-review

17 Citations (Scopus)

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.

Original language	English
Article number	104718
Journal	Tourism Management
Volume	96
DOIs	https://doi.org/10.1016/j.tourman.2022.104718
Publication status	Published - Jun 2023

Keywords

Destination function
Mobile phone data
Place design
Tourist Mobility
Travel activity type

ASJC Scopus subject areas

Development
Transportation
Tourism, Leisure and Hospitality Management
Strategy and Management

More information

10.1016/j.tourman.2022.104718

Cite this

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title = "Analyzing travel mobility patterns in city destinations: Implications for destination design",

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{\textquoteright} 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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author = "Sangwon Park and Jinyan Zu and Yang Xu and Fan Zhang and Yu Liu and Jingyan Li",

note = "Funding Information: The authors would like to express our great appreciation to Korea Tourism Organization (particularly to Team of Tourism Big Data) for their full support in initiating the project and collecting the big data set as well as sharing their insights on this project. This research was jointly supported by the Hong Kong Polytechnic University Research Grant (ZVN6) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea ( NRF2019S1A3A2098438 ). Publisher Copyright: {\textcopyright} 2023 Elsevier Ltd",

year = "2023",

month = jun,

doi = "10.1016/j.tourman.2022.104718",

language = "English",

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T2 - Implications for destination design

AU - Park, Sangwon

AU - Zu, Jinyan

AU - Xu, Yang

AU - Zhang, Fan

AU - Liu, Yu

AU - Li, Jingyan

N1 - Funding Information: The authors would like to express our great appreciation to Korea Tourism Organization (particularly to Team of Tourism Big Data) for their full support in initiating the project and collecting the big data set as well as sharing their insights on this project. This research was jointly supported by the Hong Kong Polytechnic University Research Grant (ZVN6) and the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea ( NRF2019S1A3A2098438 ). Publisher Copyright: © 2023 Elsevier Ltd

PY - 2023/6

Y1 - 2023/6

N2 - 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.

AB - 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.

KW - Destination function

KW - Mobile phone data

KW - Place design

KW - Tourist Mobility

KW - Travel activity type

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Analyzing travel mobility patterns in city destinations: Implications for destination design

Abstract

Keywords

ASJC Scopus subject areas

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