Author(s)

M. A. Arteaga Santos¹, C. Méndez Santos¹, S. Ibarra Martínez¹, J. A. Castán Rocha¹, J. Laria Menchaca¹, J. D. Terán Villanueva¹, M. G. Treviño Berrones¹, J. Pérez Cobos¹, E. Castán Rocha²

¹Faculty of Engineering, Autonomous University of Tamaulipas, Tamaulipas, Mexico.
²Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Tamaulipas, Mexico.

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 users’ 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 models’ precision and assess the best cases using Friedman’s 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 passengers’ data.

Carpooling, Machine Learning Techniques, Vehicle Traffic Congestion

Santos, M. , Santos, C. , Martínez, S. , Rocha, J. , Menchaca, J. , Villanueva, J. , Berrones, M. , Cobos, J. and Rocha, E. (2020) A Comparison of Machine Learning Techniques in the Carpooling Problem. Journal of Computer and Communications, 8, 159-169. doi: 10.4236/jcc.2020.812015.