Location choice with longitudinal WiFi data

Danalet, Antonin; Tinguely, Loïc; Lapparent, Matthieu de; Bierlaire, Michel

doi:10.1016/j.jocm.2016.04.003

Cited by 28 publications

(17 citation statements)

References 71 publications

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“…A MAC address is unique to an individual device, and it gives direct information on the movement of the enabled device along covered routes [20]. For example, from a business viewpoint, the market shares of a new catering location can be predicted by using Wi-Fi tracking to detect the sequences of activities occurring in a specific area, and the MAC address and user name capability enhance the model's location choice [21]. Plug-and-play Wi-Fi sensors [22] equipped with USB ports have been configured to collect Wi-Fi signals with the aim of detecting human presence at a specific indoor location.…”

Section: Digital Footprints In Tourism Studiesmentioning

confidence: 99%

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

et al. 2019

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The rise of radiofrequency scanner technology has led to its potential application in the observation of people’s movements. This study used a Wi-Fi scanner device to track tourists’ traveling behavior in Hokkaido’s tourism area, which occupies a large region that features a unique natural landscape. Inbound tourists have significantly increased in recent years; thus, tourism’s sustainability is considered to be important for maintaining the tourism atmosphere in the long term. Using internet-enabled technology to conduct extensive area surveys can overcome the limitations imposed by conventional methods. This study aims to use digital footprint data to describe and understand traveler mobility in a large tourism area in Hokkaido. Association rule mining (ARM)—a machine learning methodology—was performed on a large dataset of transactions to identify the rules that link destinations visited by tourists. This process resulted in the discovery of traveling patterns that revealed the association rules between destinations, and the attractiveness of the destinations was scored on the basis of visiting frequency, with both inbound and outbound movements considered. A visualization method was used to illustrate the relationships between destinations and simplify the mathematical descriptions of traveler mobility in an attractive tourism area. Hence, mining the attractiveness of destinations in a large tourism area using an ARM method integrated with a Wi-Fi mobility tracking approach can provide accurate information that forms a basis for developing sustainable destination management and tourism policies.

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Section: Digital Footprints In Tourism Studiesmentioning

confidence: 99%

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

et al. 2019

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“…As it is shown in Table 2, works related to Wi-Fi tracking techniques can be focused on different objectives: some try to obtain users' positions as accurately as possible [22][23][24][25][26][27][28][29][30][31][32], others analyze the trajectories followed by pedestrians [33][34][35], or flocks [36][37][38], and, finally, others study the Wireless Communications and Mobile Computing 5 occupation of different zones [39][40][41][42] and obtain behavior patterns [36,[43][44][45][46][47][48][49].…”

Section: Related Workmentioning

confidence: 99%

Smart Behavioral Analytics over a Low‐Cost IoT Wi‐Fi Tracking Real Deployment

Andión

Navarro

López

et al. 2018

Wireless Communications and Mobile Computing

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In a more and more urbanized World, the so-called Smart Cities need to be driven by the principles of efficiency and sustainability. Information and Communications Technologies and, in particular, the Internet of Things will play a key role on this, since they will allow monitoring and optimizing all the municipal services that exist and shall exist. People flow monitoring stands out in this context due to its wide range of applications, spanning from monitoring transport infrastructure to physical security applications. There are different techniques to perform people flow monitoring, presenting pros and cons, as in any other engineering problem. Typically, the options that provide the most accurate results are also the most expensive ones, whereas there are cases where presence detection in given areas is enough and cost is a limiting factor. The main goal of this paper is to prove that a minimal deployment of sensors, combined with the adequate analysis and visualization algorithms, can render useful results. In order to achieve this goal, a dataset is used with 1-year data from a real infrastructure composed of 9 Wi-Fi tracking sensors deployed in the Telecommunications Engineering School of Universidad Politécnica de Madrid, which is visited by 4000 people daily and covers 1.8 hectares. The data analysis includes time and occupancy, position of people, and identification of common behaviors, as well as a comparison of the accuracy of the considered solution with actual data and a video monitoring system available at the library of the school. The obtained insights can be used for optimizing the management and operation of the school, as well as for other similar infrastructures and, in general, for other kind of applications which require not very accurate people flow monitoring at low cost.

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“…These applications include: journey travel time estimation (Abedi et al, 2015), waiting time estimation of bus passenger (Kusakabe et al 2017), tracking of pedestrian trajectories (Vu et al, 2010;Musa and Eriksson, 2012), estimating origin and destination for city bus passenger (Dunlap et al, 2016) and for paratransit passenger . Danalet et al (2014) and Danalet et al (2016) recently analyzed destination detection and choice modeling. These studies, however, mainly targeted pedestrians moving within relatively small spaces (a university campus) and there are a few articles in the literature that focus on tourists travel choices in the relatively-large areas across a variety of distant sightseeing spots.…”

Section: Background and Objectivementioning

confidence: 99%

Wi-Fi based Continuous Monitoring of Tourists' Travel Behavior: Results of Two Large-Scale Field Experiments in Japan

Fukuda¹,

Ihoroi²,

Nakanishi³

et al. 2018

EasyChair Preprints

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Background and objectiveTourism would be one of the important sectors for economic and regional developments throughout the world and the tourism travel demand has been rapidly growing mainly from emerging market countries (World Travel & Tourism Council, 2017). In the case of Japan, there has been rapidly increasing trend of inbound and outbound of tourism travel trend for the last decade (Japan Tourism Agency, 2015) and it is predicted that this growth trend would continue for the next decade. In contrast with such fast increase in tourist travel demand, however, dealing with visitor congestion at many popular tourist spots would be would be one of the most urgent issues in tourism management in many countries. Several famous sightseeing sites in suffer from temporally and spatially concentrated tourist demand for their lack of capacities for attraction sites or facilities.In order to establish more appropriate tourism strategies, the data on tourists' traveler behavior (e.g. tourist site destination choices, trip-chain making and tour choices, and dwelling time at each spot) would be quite informative. Traditional paper-based questionnaire survey has been widely utilized for tourists' travel data collection but it has drawbacks in terms of inaccuracy and inefficiency, particularly when analyzing tourism demand for wide-area tourism in which tourists would travel across several distant sightseeing spots in multiple days. GPSbased survey methods, which has been popular for collecting urban travel data, may have potentials for obtaining more accurate and detailed information on tourists' traveler behavior but the method has become difficult for large spatial scale analysis because of its high costs.For cost efficiency reasons, this paper explores the feasibility of a passive travel survey data collection mainly for tourists. The recent spread of information, communication and technology (ICT) services into society offers increasing opportunities to use pervasive ICT devices (e.g., smartphones) to collect detailed location and time-of-day information of travelers, without conducting any full-scale travel surveys. At present, many people (including tourists from emerging market countries) carry their smartphones with them and these devices generally contain the functionality for access to wireless fidelity (Wi-Fi). Most importantly, each of these Wi-Fi devices has its own unique media access control (MAC) address and it would thus be possible to detect the traces of their Wi-Fi access records both temporally and spatially. This Wi-Fi based continuous monitoring of tourists with sensors installed at each sightseeing spot may exhibit innovative potentials for travel data collection if we are able to trace each MAC address.There have been several applications of Wi-Fi based approach for human monitoring or travel survey. These applications include: journey travel time estimation (Abedi et al., 2015), waiting time estimation of bus passenger (Kusakabe et al. 2017), tracking of pedestrian

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Location choice with longitudinal WiFi data

Cited by 28 publications

References 71 publications

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data

Smart Behavioral Analytics over a Low‐Cost IoT Wi‐Fi Tracking Real Deployment

Wi-Fi based Continuous Monitoring of Tourists' Travel Behavior: Results of Two Large-Scale Field Experiments in Japan

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