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

Amirian, Pouria and Basiri, Anahid and Morley, Jeremy (2016) Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft. In: 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 31 October - 3 November 2016, Burlingame, California, USA.

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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).

Item Type: Conference or Workshop Item (Paper)
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.
Keywords: Predictive analytics, navigation, movement profile, pedestrian, location-based services, personalization
Schools/Departments: University of Nottingham, UK > Faculty of Engineering
Identification Number: https://doi.org/10.1145/3003965.3003976
Related URLs:
URLURL Type
http://dl.acm.org/citation.cfm?id=3003976Publisher
Depositing User: Mellor, Alison
Date Deposited: 16 Dec 2016 09:45
Last Modified: 16 Dec 2016 09:47
URI: http://eprints.nottingham.ac.uk/id/eprint/39394

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