Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan

Phithakkitnukoon, Santi; Horanont, Teerayut; Witayangkurn, Apichon; Siri, Raktida; Sekimoto, Yoshihide; Shibasaki, Ryosuke

doi:10.1016/j.pmcj.2014.07.003

Cited by 69 publications

(50 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And Sun, Y. et al [35] empirically investigated the travel and activity patterns of active local Foursquare users in New York City. Besides the social media data mentioned above, Phithakkitnukoon, S. et al [36] also analyzed tourist behaviors in Japan using massive mobile phone GPS location records. Barkhordari,R.…”

Section: Travel Behaviorsmentioning

confidence: 99%

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Zhu

Sun

Hao

et al. 2019

IJGI

View full text Add to dashboard Cite

Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

show abstract

Section: Travel Behaviorsmentioning

confidence: 99%

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Zhu

Sun

Hao

et al. 2019

IJGI

View full text Add to dashboard Cite

show abstract

“…empirically investigates the travel and activity patterns of active local Foursquare users in New York City. And in addition, Phithakkitnukoon S et al [23] analyzed tourist behaviors in Japan using massive mobile phone GPS location records to study travel behaviors. Despite much effort put into travel behaviors mining, to have a full understanding of tourist behaviors in an area is still not an easy task.…”

Section: Travel Behaviorsmentioning

confidence: 99%

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Zhu¹,

Sun²,

Hao³

et al. 2018

Preprint

View full text Add to dashboard Cite

Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

show abstract

“…Sex distribution of the respondents was 44.4% and 55.6% by male and female, respectively. Furthermore, age distribution showed an age group of 16.9% (16)(17)(18)(19)(20) …”

Section: Dawei Special Economic Zone and Study Areamentioning

confidence: 99%

“…The increased popularity of GPS function-equipped smartphone has further enabled researchers to easily acquire human mobility data at a large scale. Mobility data coupled with Geographic Information Systems (GIS) data can be useful in transportation mode detection [13,14], purpose detection [5,8,11,15,16], and behavioral analyses [17][18][19], further enhancing empirical knowledge and information. As the GPS trip surveys provide high accurate trip data [8,20], they can be the main method of human mobility data collection among a population with GPS equipped smartphones and taxis.…”

Section: Introductionmentioning

confidence: 99%

Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar

Kimijima

Nagai

2017

Sustainability

View full text Add to dashboard Cite

Understanding human mobility patterns provides knowledge about impacts of a socio-economic transformation in a rapidly urbanizing environment. This study assesses a long-term mobility data which uses a face-to-face questionnaire and GPS logger-based method of data collection for extracting socio-economic impacts from the rapid transformation. Conversion of mobility related information such as travel distance, direction, and time from the questionnaire survey into spatiotemporal information was carried out by developing an algorithm. To illustrate the proposed approach, a case study in Dawei Special Economic Zone, Myanmar was conducted. The results show that the questionnaire-based mobility data can be associated with GPS-based mobility data and diverse mobility patterns are found for different social groups in the stage of urban formation. The results enabled an understanding of the human dynamics in interactions, which can be used for monitoring rural sustainability and its challenges in the future with the background of the accelerated project development in the area.

show abstract

Understanding tourist behavior using large-scale mobile sensing approach: A case study of mobile phone users in Japan

Cited by 69 publications

References 8 publications

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Human Mobility Analysis for Extracting Local Interactions under Rapid Socio-Economic Transformation in Dawei, Myanmar

Contact Info

Product

Resources

About