Predictive Analytics for Enhancing Travel Time Estimation in Navigation Apps of Apple, Google, and Microsoft
This addresses the problem of inaccurate travel time predictions for pedestrians in navigation apps, which is incremental as it builds on existing methods by incorporating movement profiles.
The paper evaluated pedestrian travel time predictions in major navigation apps and found they do not learn from individual movement profiles and suffer from data quality issues like missing pedestrian crossing information. It demonstrated that using predictive analytics models to learn from movement profiles can improve estimation accuracy.
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.