Profiling the Mobility of Tourists Exploring Social Sensing

Ferreira, Ana Paula Gomes; Silva, Thiago H.; Loureiro, Antônio A. F.

doi:10.1109/dcoss.2019.00100

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…The present study significantly builds upon our previous work [26] in several directions. Differently from all the previous works, in this study, we propose an approach to identify mobility patterns, which help to better understand the profile of tourists, in particular, properties of their mobility.…”

Section: Mobility Of Tourists and Residentsmentioning

confidence: 65%

Uncovering Spatiotemporal and Semantic Aspects of Tourists Mobility Using Social Sensing

Ferreira¹,

Silva²,

Loureiro³

2020

Preprint

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Tourism favors more economic activities, employment, revenues and plays a significant role in development; thus, the improvement of this activity is a strategic task. In this work, we show how social sensing can be used to understand the key characteristics of the behavior of tourists and residents. We observe distinct behavioral patterns in those classes, considering the spatial and temporal dimensions, where cultural and regional aspects might play an important role.Besides, we investigate how tourists move and the factors that influence their movements in London, New York, Rio de Janeiro and Tokyo. In addition, we propose a new approach based on a topic model that enables the automatic identification of mobility pattern themes, ultimately leading to a better understanding of users' profiles. The applicability of our results is broad, helping to provide better applications and services in the tourism segment.

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Section: Mobility Of Tourists and Residentsmentioning

confidence: 65%

Uncovering Spatiotemporal and Semantic Aspects of Tourists Mobility Using Social Sensing

Ferreira¹,

Silva²,

Loureiro³

2020

Preprint

Self Cite

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“…Note that users are only considered tourists if we can spot a home country according to our approach. Previous studies in the literature have successfully applied similar strategies [13], [42], [45], and [46].…”

Section: End Formentioning

confidence: 99%

PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles

et al. 2022

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This paper proposes PredicTour, an approach to process check-ins made by users of locationbased social networks (LBSNs), and predict mobility patterns of tourists visiting new countries with or without previous visiting records. PredicTour is composed of three key parts: mobility modeling, profile extraction, and tourist mobility prediction. In the first part, sequences of check-ins within a time interval are associated with other user information to produce a new structure called "mobility descriptor". In the profile extraction, self-organizing maps and fuzzy C-means work jointly to group users according to their mobility descriptors. PredicTour then identifies tourist profiles and estimates mobility patterns of tourists visiting new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists' mobility. The proposed approach can be used in different applications, such as in recommender systems for tourists or in decision-making support for urban planners interested in improving tourists' experiences.

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“…Several current survey papers on physical sensing have investigated the functionality and features of recent physical sensing approaches (e.g., roadside surveillance systems, wildfire monitoring systems, indoor localization using wireless networks) (Lee and Gerla 2010; Zafari et al, 2019). On the same note, several survey papers on social sensing have provided comparative studies on representative social sensing schemes (e.g., fuel availability finder using crowdsensing apps, social media-driven interesting place discovery) (Ferreira et al, 2019; Xintong et al, 2014; Li et al, 2016). While a few survey papers have explored some sensing approaches that fall at the intersection of social sensing and physical sensing and are partially related to SPS (Shi et al, 2011; Zeng et al, 2020; Dressler 2018), they do not focus on an extensive overview of the SPS paradigm itself or present a comparative study of existing SPS applications.…”

Section: Introductionmentioning

confidence: 99%

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

Rashid

Wei

Wang

2023

Collective Intelligence

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Propelled by the omnipresence of versatile data capture, communication, and computing technologies, physical sensing has revolutionized the avenue for decisively interpreting the real world. However, various limitations hinder physical sensing’s effectiveness in critical scenarios such as disaster response and urban anomaly detection. Meanwhile, social sensing is contriving as a pervasive sensing paradigm leveraging observations from human participants equipped with portable devices and ubiquitous Internet connectivity to perceive the environment. Despite its virtues, social sensing also inherently suffers from a few drawbacks (e.g., inconsistent reliability and uncertain data provenance). Motivated by the complementary strengths of the two sensing modes, social-physical sensing (SPS) is protruding as an emerging sensing paradigm that explores the collective intelligence of humans and machines to reconstruct the “state of the world,” both physically and socially. While a good number of interesting SPS applications have been studied, several critical unsolved challenges still exist in SPS. In this paper, we provide a comprehensive survey of SPS, emphasizing its definition, key enablers, state-of-the-art applications, potential research challenges, and roadmap for future work. This paper intends to bridge the knowledge gap of existing sensing-focused survey papers by thoroughly examining the various aspects of SPS crucial for building potent SPS systems.

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Profiling the Mobility of Tourists Exploring Social Sensing

Cited by 7 publications

References 24 publications

Uncovering Spatiotemporal and Semantic Aspects of Tourists Mobility Using Social Sensing

Uncovering Spatiotemporal and Semantic Aspects of Tourists Mobility Using Social Sensing

PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles

A survey on social-physical sensing: An emerging sensing paradigm that explores the collective intelligence of humans and machines

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