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The way we walk
Alexandra Millonig
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ConclusionPreliminary results of the first empirical phase indicate that a number of homogenous behaviour patterns can be observed, especially in consistent context situations. Further investigations using a non-disguised form of observation combined with detailed interviews include and currently test basic findings of the first analyses. Further empirical analyses of more data during the currently ongoing second empirical phase as well as a careful examination of the results in different context situations during the final stage of the study are expected to lead to a comprehensive interpretation of pedestrian spatio-temporal behaviour. This can on the one hand be used in future mobile navigation services to provide customised route suggestions and location based information, and on the other hand also serve as a basis for determining parameters for pedestrian simulation models. AcknowledgementsThis work is part of the “UCPNavi” project, a cooperation project between the Vienna University of Technology and arsenal research, Vienna. The project is supported by the Austrian Funds for Scientific Research (FWF). The author would like to thank M. Ray (arsenal research) for developing the shadowing tool and N. Brändle (arsenal research) for his help and advice concerning data analysis. The digital map used in Figure 2 has been provided by Stadt Wien – ViennaGIS (www.wien.gv.at/viennagis/). References[1] Millonig, A. and Gartner, G. (2007). On Defining Pedestrian Typologies for Customised Mobile Information Services. In Proceedings of the 4th International Symposium on Location Based Services & TeleCartography. [2] Helbing, D., Molnár, P., Farkas, I.J., Bolay, K. (2001). Self-organizing pedestrian movement. Environment and Planning B: Planning and Design 2001 28, pp. 361-383. [3] Thomas, C. (2003). Zu Fuß einkaufen. Project report. [4] Millonig A., Schechtner K. (2007). Decision Loads and Route Qualities for Pedestrians – Key Requirements for the Design of Pedestrian Navigation Services. In: Waldau, N., Gattermann, P., Knoflacher, H., Schreckenberg, M. (eds.): Pedestrian and Evacuation Dynamics 2005. Springer Berlin Heidelberg, pp. 109-118. [5] Golledge, R. G. (1995): Defining the criteria used in path selection, Technical Report UCTC No. 78, University of California Transportation Center. [6] Hill, M. (1984). Stalking the Urban Pedestrian: A Comparison of Questionnaire and Tracking Methodologies for Behavioral Mapping in Large- Scale Environments. Environment and Behavior 16, pp. 539-550. [7] Shoval, N., Isaacson, M. (2007). Tracking Tourists in the Digital Age. Annals of Tourism Research, 34 (1), pp. 141–159. [8] Spek, S.C. van der (2007). Legible City – Walkable City – Liveable City: Observation of Walking Patterns in City Centres. Introductory paper, Urbanism On Track – Expert meeting on the application in urban design and planning of GPS-based and other tracking-based research, Delft, The Netherlands. [9] Svetsuk, A. (2007). Experiments in urban mobility analysis in Rome using mobile phone data. Position paper, Urbanism On Track – Expert meeting on the application in urban design and planning of GPS-based and other trackingbased research, Delft, The Netherlands. [10] Daamen, W., Hoogendoorn, S.P. (2003). Research on pedestrian traffic flows in the Netherlands, Proceedings Walk 21 IV. Portland, Oregon, United States: Walk 21 conference, pp. 101-117. [11] O’Connor, A., Zerger, A., Itami, R. (2005). Geo-Temporal Tracking and Analysis of Tourist Movement. Mathematics and Computers in Simulation 69, pp. 135-150. [12] Jakob, A. (2001). Möglichkeiten und Grenzen der Triangulation quantitativer und qualitativer Daten am Beispiel der (Re-) Konstruktion einer Typologie erwerbsbiographischer Sicherheitskonzepte. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research (On-line Journal), 2(1). |
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