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
navigation apps (often called Maps), use a variety of available data sources to
calculate and predict the travel time as well as several options for routing in
public transportation, car or pedestrian modes. This paper evaluates the
pedestrian mode of Maps apps in three major smartphone operating systems
(Android, iOS and Windows Phone). In the paper, we will show that the Maps apps
on iOS, Android and Windows Phone in pedestrian mode, predict travel time
without learning from the individual's movement profile. In addition, we will
exemplify that those apps suffer from a specific data quality issue which
relates to the absence of information about location and type of pedestrian
crossings. Finally, we will illustrate learning from movement profile of
individuals using various predictive analytics models to improve the accuracy
of travel time estimation.
