2015
Conference article  Restricted

Social or green? A data­driven approach for more enjoyable carpooling

Guidotti R., Sassi A., Berlingerio M., Pascale A.

Mobility and Social Behavior  Carpooling 

Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent works on carpooling and journey planning take into account, besides mobility match, also social aspects and, more generally, non-monetary rewards. In line with this, we presenta data-driven methodology for a more enjoyable carpooling. We introduce a measure of enjoyability based on people's interests,social links, and tendency to connect to people with similar or dissimilar interests. We devise a methodology to compute enjoyability from crowd-sourced data, and we show how this can be used on real world datasets to optimize for both mobility and enjoyability. Our methodology was tested on real data from Rome and San Francisco. We compare the results of an optimization model minimizing the number of cars, and a greedy approach maximizing the enjoyability. We evaluate them in terms of cars saved, and average enjoyability of the system. We present also the results of a user study, with more than 200 users reporting an interest of 39% in the enjoyable solution. Moreover, 24%of people declared that sharing the car with interesting people would be the primary motivation for carpooling.

Source: 18th IEEE Intelligent Transportation Systems Conference, pp. 842–847, Las Palmas de Gran Canaria, Spain, 15-18/09/2015


Metrics



Back to previous page
BibTeX entry
@inproceedings{oai:it.cnr:prodotti:345079,
	title = {Social or green? A data­driven approach for more enjoyable carpooling},
	author = {Guidotti R. and Sassi A. and Berlingerio M. and Pascale A.},
	doi = {10.1109/itsc.2015.142},
	booktitle = {18th IEEE Intelligent Transportation Systems Conference, pp. 842–847, Las Palmas de Gran Canaria, Spain, 15-18/09/2015},
	year = {2015}
}

PETRA
Personal Transport Advisor: an integrated platform of mobility patterns for Smart Cities to enable demand-adaptive transportation systems


OpenAIRE