Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft
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 s...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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2016
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| Online Access: | https://eprints.nottingham.ac.uk/39394/ |
| _version_ | 1848795826835423232 |
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| author | Amirian, Pouria Basiri, Anahid Morley, Jeremy |
| author_facet | Amirian, Pouria Basiri, Anahid Morley, Jeremy |
| author_sort | Amirian, Pouria |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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). |
| first_indexed | 2025-11-14T19:38:16Z |
| format | Conference or Workshop Item |
| id | nottingham-39394 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:38:16Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-393942020-05-04T18:14:26Z https://eprints.nottingham.ac.uk/39394/ Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft Amirian, Pouria Basiri, Anahid Morley, Jeremy 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). 2016-10-31 Conference or Workshop Item PeerReviewed Amirian, Pouria, 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. Predictive analytics navigation movement profile pedestrian location-based services personalization http://dx.doi.org/10.1145/3003965.3003976 10.1145/3003965.3003976 10.1145/3003965.3003976 10.1145/3003965.3003976 |
| spellingShingle | Predictive analytics navigation movement profile pedestrian location-based services personalization Amirian, Pouria Basiri, Anahid Morley, Jeremy Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title | Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title_full | Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title_fullStr | Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title_full_unstemmed | Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title_short | Predictive analytics for enhancing travel time estimation in navigation apps of Apple, Google, and Microsoft |
| title_sort | predictive analytics for enhancing travel time estimation in navigation apps of apple, google, and microsoft |
| topic | Predictive analytics navigation movement profile pedestrian location-based services personalization |
| url | https://eprints.nottingham.ac.uk/39394/ https://eprints.nottingham.ac.uk/39394/ https://eprints.nottingham.ac.uk/39394/ |