The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city
By Daniele Quercia, Rossano Schifanella, Luca Maria Aiello. ACM Conference on Hypertext & Social Media, 2014
464 citations recorded by the source corpus.
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.
Subfields: Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/46224e2d514ffc900cfef332cd7447dbd7111b78
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Citations in the corpus
- DeepCity: A Feature Learning Framework for Mining Location Check-Ins (2016, methodology): “Researchers have also used check-in data to measure the happiness [19] and walkability [18] of streets, and find the similar neighborhoods across different cities [7].”
- Algorithms (and the) everyday (2017, background): “The goal of the travel algorithm was to ‘ suggest routes that are not only short but also emotionally pleasant ’ (Quercia, Schifanella, & Aiello, 2014, p. 1).”