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Papoutsis, P., Michel, B., Philippe, A. and Duong, T. (2020) Bayesian Hierarchical Models for the Prediction of the Driver Flow and Passenger Waiting Times in a Stochastic Carpooling Service.
https://arxiv.org/abs/2007.08962

has been cited by the following article:

• TITLE: A Comparison of Machine Learning Techniques in the Carpooling Problem

  AUTHORS: M. A. Arteaga Santos, C. Méndez Santos, S. Ibarra Martínez, J. A. Castán Rocha, J. Laria Menchaca, J. D. Terán Villanueva, M. G. Treviño Berrones, J. Pérez Cobos, E. Castán Rocha

  KEYWORDS: Carpooling, Machine Learning Techniques, Vehicle Traffic Congestion

  JOURNAL NAME: Journal of Computer and Communications, Vol.8 No.12, December 25, 2020

  ABSTRACT: 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.