%0 Journal Article
%T A Comparison of Machine Learning Techniques in the Carpooling Problem
%A M. A. Arteaga Santos
%A C. M¨Śndez Santos
%A S. Ibarra Mart¨Şnez
%A J. A. Cast¨˘n Rocha
%A J. Laria Menchaca
%A J. D. Ter¨˘n Villanueva
%A M. G. TreviŁżo Berrones
%A J. P¨Śrez Cobos
%A E. Cast¨˘n Rocha
%J Journal of Computer and Communications
%P 159-169
%@ 2327-5227
%D 2020
%I Scientific Research Publishing
%R 10.4236/jcc.2020.812015
%X Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the usersĄŻ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the modelsĄŻ precision and assess the best cases using FriedmanĄŻs test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengersĄŻ data.
%K Carpooling
%K Machine Learning Techniques
%K Vehicle Traffic Congestion
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=106121