Mobility Choices—An Instrument for Precise Automatized Travel Behavior Detection & Analysis

Thomas Feilhauer, Florian Braun, Katja Faller, David Hutter, Daniel Mathis, Johannes Neubauer, Jasmin Pogatschneg and Michelle Weber
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Thomas Feilhauer: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Florian Braun: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Katja Faller: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
David Hutter: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Daniel Mathis: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Johannes Neubauer: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Jasmin Pogatschneg: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria
Michelle Weber: FH Vorarlberg, University of Applied Sciences, 6850 Dornbirn, Austria

Sustainability, 2021, vol. 13, issue 4, 1-23

Abstract: Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires.

Keywords: Voluntary Travel Behavioral Change; travel behavior analysis; transportation mode detection; alternative routing; anonymizing recorded tracks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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