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

TL;DR

This paper evaluates the travel time prediction accuracy of Maps apps on major smartphone platforms in pedestrian mode, identifies data quality issues, and proposes learning from individual movement profiles to enhance estimation accuracy.

Contribution

It highlights the lack of personalized learning in current pedestrian travel time predictions and demonstrates how predictive analytics can improve accuracy.

Findings

Maps apps predict travel time without personal movement data.

Data quality issues include missing crossing information.

Learning from individual profiles improves travel time estimates.

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 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.