The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities

Abstract

In this paper, proximity-based Bluetooth tracking is postulated as an efficient and effective methodology for analysing the complex spatiotemporal dynamics of visitor movements at mass events. A case study of the Ghent Festivities event (1.5 million visitors over 10 days) is described in detail and preliminary results are shown to give an indication of the added value of the methodology for stakeholders of the event. By covering 22 locations in the study area with Bluetooth scanners, we were able to extract 152,487 trajectories generated by 80,828 detected visitors. Apart from generating clear statistics such as visitor counts, the share of returning visitors, and visitor flow maps, the analyses also reveal the complex nature of this event by hinting at the existence of several mutually different visitor profiles. We conclude by arguing why Bluetooth tracking offers significant advantages for tracking mass event visitors with respect to other and more prominent technologies, and outline some of its remaining deficiencies. KeywordsBluetooth, tracking, mass event, geographical information technology, geographical information systems IntroductionIn the last few years the representation and analysis of large volumes of trajectory information of objects moving through geographical space has become a major topic of interest in research domains such as geographical information science (Ahlqvist et al., 2010;Shaw, Yu, & Bombom, 2008), computer science (Bogorny, Kuijpers, & Alvares, 2009;Orlando et al., 2007), visual analytics (Andrienko & Andrienko, 2007 and urbanism (Van Schaick & Van der Spek, 2008). This burgeoning academic interest has emerged as a result of the increased feasibility and affordability of collecting detailed data about spatiotemporal phenomena triggered by the widespread adoption of location-aware technologies. Past studies have focused on the movements of various kinds of objects including vehicles (Quiroga & Bullock, 1998), animals (Laube et al., 2007, bank notes (Brockmann, Hufnagel, & Geisel, 2006) and typhoons (Terry & Feng, 2010), but the majority of research has been devoted to human movement in different contexts and at various scales. Some examples are the movement of athletes on a pitch (Laube, Imfeld, & Weibel, 2005), tourists on a regional (Ahas et al., 2008) and local scale (Kemperman, Borgers, & Timmermans, 2009; O'Connor, Zerger, & Itami, 2005;Shoval & Isaacson, 2007a), and customers in a supermarket (Hui, Fader, & Bradlow, 2009). In these contexts, advanced tracking technologies complement more traditional qualitative methods, such as shadowing (Quinlan, 2008) and collecting travel diaries (Axhausen et al., 2002).Within research on human behaviour, particular attention has been devoted to the collective behaviour of crowds at mass events such as street parades, festivals, public assemblies, sporting events, and exhibitions (Batty, Desyllas, & Duxbury, 2003;Helbing, Johansson, & Al-Abideen, 2007;Zeitz et al., 2009). Tragic events such as the recent stampede during the 2010 edi...

Intelligent Event Management with Bluetooth Sensor Networks

“…During its first edition in 2011, the event drew over 200.000 visitors over 3 days causing unexpected challenges for the organizers. In order to cope more rationally with these challenges in the future, and due to our previous experiences with large events organized in Ghent [8], a cooperation was set up with our research group.…”

“…Bluetooth tracking has already been used to study the spatiotemporal dynamics of crowds in a variety of settings, such as during the Ghent Festivities 2010 where around 150.000 trajectories over 10 days were generated, accounting for around 10% of the total visitor population being tracked [8]. In the near future the methodology will be used to generate and disseminate realtime information during the Ghent Light Festival 2012.…”

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

“…Devices enabled with pervasive computing technologies, such as, smart cards [1], [2], wearable devices [3], Radio-Frequency (RF) communication devices (RFCD) (e.g., mobile phones [4], [5], [6], Bluetooth [7], [8], [9], [10], Wireless Fidelity (WiFi) [11], [12], [13], RadioFrequency ID (RFID), and Global Positioning System (GPS) [14], [15], [16]), Optical-Wireless communication (OWC) (e.g., infrared or IR devices) [17], video surveillance [18], [19], [20], in conjunction with social media [21], [22], [23] and different event websites are part and parcel of our daily lives. Recent studies report that the above mentioned ubiquitous technologies can also be employed as sensors to collect data on human activities from the urban space and are uploaded to distributed / centralized databases.…”

“…The main focus of these articles were the methods that describe detection of crowd dynamics provided by crowd data. These dynamics generally consist of crowd count [6], [7], [11], [13], [49], [50], [17], [51], [52], crowd trajectories/mobility [32], [53], [9], [14], [54], crowd sentiment [21], and crowd behavior [18], [20], [55], [56], [57], [58], [51], [59], [60]. These articles clearly showed an increasing trend in the crowd event detection research for UA (see Figure 1C).…”

Measuring the exitability of buildings: A new perspective on indoor accessibility

“…Research of inner-city mobility (Antipova, et al, 2011;Keeling, 2008), accessibility analysis and studies of optimal time-space distributions (Kwan & Weber, 2003;Neutens, et al, 2012;Neutens, et al, 2010;Versichele, et al, 2012) all reveal elements of the spatial distribution and interactions of people and businesses within the two-dimensional urban city.…”