Mining travel behaviors of tourists with mobile phone data: A case study in Hainan

Ling, Feng; Sun, Tianyue; Zhu, Xinning; Chen, Qingqing; Tang, Xiaosheng; Xia, Ke

doi:10.1109/compcomm.2016.7924957

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…(ii) a scale-adaptive method for refining the location of activities within outdoor and indoor environments. Cited research works location event log preparation and interpretation (Senderovich et al, 2016;Muzammal et al, 2018;Wan et al, 2017;Namaki Araghi et al, 2018a) Use of location data for extraction of knowledge (Hwang and Jang, 2017;Sztyler et al, 2016;Ertek et al, 2017;Mazimpaka and Timpf, 2016), (Rojas et al, 2017a;Zheng, 2015a;Tanuja and Govindarajulu, 2017;Ramos et al, 2017;Garaeva et al, 2017;Bao and Wang, 2017), (Zhenjiang et al, 2017;Aryal and Sujing Wang, 2017;Tanuja and Govindarajulu, 2016;Feng Ling et al, 2016), (Lamr and Skrbek, 2016;Blank et al, 2016;Fernandez-Llatas et al, 2015;Zheng, 2015b), (Liao et al, 2015;Tang et al, 2015;Miclo et al, 2015;Jin et al, 2015;Martinez-Millana et al, 2019), (Dogan et al, 2019;Namaki Araghi et al, 2019;Namaki Araghi et al, 2018a;Namaki Araghi et al, 2018b;Araghi et al, 2018) Figure 3: Analysis of the literature of process mining, indoor localization systems, and data mining relevant to the approaches in which the location data were used.…”

Section: Preparation and Interpretation Of Location Datamentioning

confidence: 99%

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi,

Fontanili,

Lamine

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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The application of indoor localization and process mining emerges as an intriguing tool for the researchers to address the structural issues related to the patient pathways inside healthcare organizations. However, there is a major gap in the literature. This is related to the lack of enough attention to the interpretation of location data. Therefore, as a contribution, this article presents the DIAG meta-model and relevant location data interpretation rules. This model-driven approach has been realized in the context of the R-IOSUITE application and it supports the further analyses by the process mining methods.

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Section: Preparation and Interpretation Of Location Datamentioning

confidence: 99%

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Araghi,

Fontanili,

Lamine

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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Predictive Analysis for Modeling Travel Decision Making

Keerthi

Puzhankara

2018

2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)

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Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

Sun

Chen

Jin

et al. 2022

Journal of Advanced Transportation

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With the swift development of tourism all around the world, it has become vital to improve the recommendation of useful travel information to tourists to assure their convenience and satisfaction. In this paper, we propose a novel multi-objective optimal travel route recommendation framework, which collects tourists’ travel trajectories from their mobile phone signaling data. Then, the proposed framework preprocesses the mobile signaling data to transform raw trajectories into tourists’ travel sequences. Subsequently, the framework finds the popular attractions and frequent travel routes from the travel pattern sequences by using a frequent pattern mining method. Finally, an improved ant colony optimization (ACO) algorithm with a novel extensible heuristic factor approach is adopted to search the multi-objective optimal travel routes according to the popularity of attractions and travel time of tourists. The experimental results indicate that the proposed framework is efficient in recommending multi-objective optimal travel routes considering tourists’ travel time and attractions’ popularity while ensuring that the recommended travel route is suitable.

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Mining travel behaviors of tourists with mobile phone data: A case study in Hainan

Cited by 3 publications

References 11 publications

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Interpretation of Patients’ Location Data to Support the Application of Process Mining Notations

Predictive Analysis for Modeling Travel Decision Making

Multi-Objective Optimal Travel Route Recommendation for Tourists by Improved Ant Colony Optimization Algorithm

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