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Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft

Amirian, Pouria; Basiri, Anahid; Morley, Jeremy

Authors

Pouria Amirian

Anahid Basiri

Jeremy Morley



Abstract

The explosive growth of the location-enabled devices coupled with the increasing use of Internet services has led to an increasing awareness of the importance and usage of geospatial information in many applications. The mobile navigation apps (often called “Maps”), use a variety of available data sources to calculate and predict the travel time for different modes. This paper evaluates the pedestrian mode of Maps apps in three major smartphone operating systems (Android, iOS and Windows Phone). We will demonstrate that the Maps apps on iOS, Android and Windows Phone in pedestrian mode, predict travel time without learning from the individual’s movement profile. Then, we will exemplify that those apps suffer from a specific data quality issue (the absence of information about location and type of pedestrian crossings). Finally, we will illustrate learning from movement profile of individuals using predictive analytics models to improve the accuracy of travel time estimation for each user (personalization).

Citation

Amirian, P., Basiri, A., & Morley, J. (2016). Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft. In Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS '16 (31-36). https://doi.org/10.1145/3003965.3003976

Conference Name 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science
Conference Location Burlingame, California, USA
Start Date Oct 31, 2016
End Date Nov 3, 2016
Acceptance Date Oct 16, 2016
Online Publication Date Oct 31, 2016
Publication Date Oct 31, 2016
Deposit Date Dec 16, 2016
Publicly Available Date Mar 28, 2024
Peer Reviewed Peer Reviewed
Pages 31-36
Book Title Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science - IWCTS '16
ISBN 9781450345774
DOI https://doi.org/10.1145/3003965.3003976
Keywords Predictive analytics, navigation, movement profile, pedestrian, location-based services, personalization
Public URL https://nottingham-repository.worktribe.com/output/820237
Publisher URL http://dx.doi.org/10.1145/3003965.3003976
Related Public URLs http://dl.acm.org/citation.cfm?id=3003976
Additional Information Published in: Proceedings of the 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, p. 31-36. New York : ACM, 2016. ISBN: 978-1-4503-4577-4.

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