6 research outputs found
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
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 âMapsâ), 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 individualâs 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)
A criteria based function for reconstructing low-sampling trajectories as a tool for analytics
Abstract: Mobile applications equipped with Global Positioning Systems have generated a huge quantity of location data with sampling uncertainty that must be handled and analyzed. Those location data can be ordered in time to represent trajectories of moving objects. The data warehouse approach based on spatio-temporal data can help on this task. For this reason, we address the problem of personalized reconstruction of low-sampling trajectories based on criteria over a graph for including criteria of movement as a dimension in a trajectory data warehouse solution to carry out analytical tasks over moving objects and the environment where they moveMaestrĂ