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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...

The use of Bluetooth technology as a technique to collect data about the movement of individuals is increasingly gaining attention. This paper explores the potential of sequence alignment methods to analyse data obtained from Bluetooth tracking. To this end, an empirical case study is elaborated which applies sequence alignment methods to examine the behavioural patterns of visitors tracked by Bluetooth at a huge trade fair in Belgium. The results and findings underline both the validity of Bluetooth tracking to collect data from visitors at mass events, as well as the ability of sequence alignment methods to extract insightful information from sequences within such data.

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Time intervals are conventionally represented as linear segments in a onedimensional space. An alternative representation of time intervals is the Triangular Model (TM), which represents time intervals as points in a two-dimensional space. In this paper, the use of TM in visualising and analysing time intervals is investigated. Not only does this model offer a compact visualisation of the distribution of intervals, it also supports an innovative temporal query mechanism that relies on geometries in the two-dimensional space. This query mechanism has the potential to simplify queries that are hard to specify using traditional linear temporal query devices. Moreover, a software prototype that implements TM in a geographical information system (GIS) is introduced. This prototype has been applied in a real scenario to analyse time intervals that were detected by a Bluetooth tracking system. This application shows that TM has potential of supporting a traditional GIS to analyse interval-based geographical data.

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Accurate spatiotemporal information on crowds is a necessity for a better management in general and for the mitigation of potential security risks. The large numbers of individuals involved and their mobility, however, make generation of this information non-trivial. This paper proposes a novel methodology to estimate and map crowd sizes using mobile Bluetooth sensors and examines to what extent this methodology represents a valuable alternative to existing traditional crowd density estimation methods. The proposed methodology is applied in a unique case study that uses Bluetooth technology for the mobile mapping of spectators of the Tour of Flanders 2011 road cycling race. The locations of nearly 16,000 cell phones of spectators along the race course were registered and detailed views of the spatiotemporal distribution of the crowd were generated. Comparison with visual head counts from camera footage delivered a detection ratio of 13.0 ± 2.3%, making it possible to estimate the crowd size. To our knowledge, this is the first study that uses mobile Bluetooth sensors to count and map a crowd over space and time.