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The explosive growth of the location-enabled devices coupled with theincreasing use of Internet services has led to an increasing awareness of theimportance and usage of geospatial information in many applications. Thenavigation apps (often called Maps), use a variety of available data sources tocalculate and predict the travel time as well as several options for routing inpublic transportation, car or pedestrian modes. This paper evaluates thepedestrian mode of Maps apps in three major smartphone operating systems(Android, iOS and Windows Phone). In the paper, we will show that the Maps appson iOS, Android and Windows Phone in pedestrian mode, predict travel timewithout learning from the individual's movement profile. In addition, we willexemplify that those apps suffer from a specific data quality issue whichrelates to the absence of information about location and type of pedestriancrossings. Finally, we will illustrate learning from movement profile ofindividuals using various predictive analytics models to improve the accuracyof travel time estimation.

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