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A. Millonig, N. Brändle, G. Gartner:
"Supporting pedestrian wayfinding by analysing movement patterns";
Talk: 24th International Cartographic Conference, Santiago, Chile; 2009-11-15 - 2009-11-21; in: "The Worldīs Geo-Spatial Solutions", (2009), ISBN: 978-1-907075-02-5; 11 pages.

In the field of cartography, recent years are believed to have brought revolutions as
profound as any in the past. Especially in the field of mobile navigation tools, digital
maps, mobile devices with increasingly large displays, and ubiquitously accessible,
location-based information continuously advance and push the development of efficient
wayfinding services. Furthermore, recent advances in collecting and including
volunteered, user-generated content in web services rapidly increase the amount of
potentially available geographical information. This offers new opportunities for
emerging mobile pedestrian navigation services: in contrast to common navigation tools
used for vehicles, navigation services for pedestrians must fulfil more complex
requirements in order to be accepted. For pedestrians, the shortest path does not always
represent the optimal route for an individualīs purposes. Studies have revealed that
people often forgo to take the shortest path and prefer the "most beautiful", "most
convenient", or "safest path". People exploring a new environment on foot would
therefore especially benefit from systems providing information concerning route
qualities, interesting facilities in the vicinity, and other useful location-related
suggestions.
The vast and increasing amount of potentially useful information, however, also
involves some negative effects. Overabundance of information may easily hinder
effective information extraction. Hence successful wayfinding and information services
have to take all the above factors into account in order to efficiently support pedestrian
wayfinding and to avoid potential information overload. However, the goal of
developing efficient, customised navigation services can only be achieved by
comprehensively investigating human spatio-temporal behaviour and related influence
factors.
As part of the scientific project UCPNavi we investigate group-specific spatio-temporal
behaviour in order to set the basis for developing customised mobile information
services. The study aims at the development of a typology of pedestrian mobility styles
based on the observation and analysis of pedestrian walking patterns, route-choice
determinants and additional relevant influence factors (e.g. lifestyle-related attributes
such as general habits, attitudes, and preferences). In order to thoroughly comprehend
pedestrian spatio-temporal behaviour, an eclectic approach to investigate walking
behaviour is necessary. Therefore, we use an "across-method" triangulation approach
combining various complementary empirical methods of data collection and analysis to
explore the subject from different perspectives. Within this two-stage approach, we
have collected more than 100 datasets by shadowing and 130 questionnaires during the
first empirical phase, and more than 100 trajectories using GPS or Bluetooth and more
than 250 interview datasets during the second phase of the study.
In this contribution we present results of motion and interview data analysis based on
data collected in a shopping mall and a shopping street. We also introduce an initial
pedestrian typology based on qualitative-interpretative and quantitative-statistical data.
Types are described according to characteristic attributes identified by route choice
behaviour, walking patterns and interest foci. The relevant factors include velocities,
stopping behaviour, categories of visited facilities, and individual preferences (general
interests, preferences concerning qualities of routes or environments, orientation
strategies, etc.). Furthermore, we highlight differences in the outcomes resulting from
data collected by different empirical methods and in different investigation areas
(indoor and outdoor).
The resulting typology of lifestyle-based pedestrian mobility styles and the identified
characteristic attributes can serve as a basis to create pedestrian interest profiles in
ubiquitous environments and to customise navigational and environmental information
for mobile applications in order to fulfil individual needs. Moreover, a profound
understanding of walking patterns and related influence factors is also beneficial for
other research fields. Group-related behaviour patterns are crucial for the development
of efficient geoinformation products, for the design of attractive urban environments,
and for the determination of specific group-related parameters in agent-based simulation
models.