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Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple, Google, and Microsoft

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. (pp. pp. 31-36). ACM: Burlingame, CA, USA. Green open access

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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).

Type: Proceedings paper
Title: Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple, Google, and Microsoft
Event: 9th ACM SIGSPATIAL International Workshop on Computational Transportation Science (IWCTS)
Location: Burlingame, CA
Dates: 31 October 2016
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3003965.3003976
Publisher version: https://doi.org/10.1145/3003965.3003976
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Information Systems, Transportation Science & Technology, Computer Science, Transportation, Predictive Analytics, Navigation, Movement Profile, Pedestrian, Location-Based Services, Personalization
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10061247
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