Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

C. Rudloff, M. Ray:
"Detecting travel modes and profiling commuter habits solely based on GPS data";
Vortrag: Transportation Research Board 89th annual meeting, WAshington DC, USA; 09.01.2010 - 14.01.2010; in: "TRB 89th Annual Meeting - Compendium of papers", Transportation Research Board (Hrg.); (2010), Paper-Nr. 10-0634.

Kurzfassung englisch:
The Global Positioning System (GPS) has been gaining importance for travel surveys since the
1990s. While it is successfully used to collect accurate information about travelled routes and
travel times, only little is known on extracting added information like transport modes and trip
purposes. In this paper a system for collecting and profiling commuter data is presented. This
includes all steps from determining the right GPS device to processing the GPS trajectories using
a data driven approach without relying on geographic information systems (GIS) or user input. A
participantīs habit in route choice, travel times, travel mode changes and travel modes is
extracted and stored in a profile. Using this, at the beginning of a commute, routes that the
commuter is likely to take are determined and used to provide personalized real-time time traffic
Extracting the commuter profile includes pre-processing methods for the trajectories,
detection methods for places of travel mode changes and segmentation of tracks into singular
travel modes. Also a detailed mode detection step is performed, comparing decision trees,
logistic regression, multilayer perceptrons and support vector machines as classification methods.
Finally, the data is reduced to a network of prototypes using Growing Neural Gas (GNG), a selforganizing-
network algorithm. This in turn enables a more effective algorithm for detecting
likely routes.
The mode detection algorithms achieved a detection rate of about 84% on a test sample,
while the commuting-routes were predicted correctly 80% of the time within five minute from
the start of the commute.

Elektronische Version der Publikation:
Detecting travel modes and profiling commuter habits solely based on GPS data

Erstellt aus der Publikationsdatenbank des AIT Austrian Institute of Technology.