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Efficiency Assessment of Public Transport Vehicles Using Machine Learning and Non-parametric Models

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Industrial Engineering and Operations Management (IJCIEOM 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 400))

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Abstract

The transportation sector is essential for today’s global economy, as it tan-gents a wide range of issues such as mobility, urban planning, and economic development. Understanding the performance of vehicles is fundamental for the Brazilian economy since millions of passengers are carried by public transport every day, and this sector represents a significant share of the national GDP. Although in literature, there is a range of suitable approaches for efficiency analysis, the fourth industrial revolution has leveraged the way of acquiring data (e.g., via digital technologies), bringing the need for more advanced data analytics models to explore and process the data beforehand, as well as dealing with uncertainty. In this sense, this paper aims to provide a novel approach to assessing the efficiency of public transport vehicles by combining fuzzy clustering and Data Envelopment Analysis models in a real case study with data from embedded sensors in buses in Rio de Janeiro. A more robust integrated approach for evaluating operational efficiency can assist decision-makers and consumers in better comprehending the relationship between energy (fuel) consumption and bus efficiencies. This could enable the authorities and public transport management departments to develop appropriate policies and strategies and to reconstruct certain features of the inefficient routes, thereby increasing the operational efficiency of land transportation, reducing mobility costs, and even decreasing the carbon footprint.

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Acknowledgments

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil - CAPES [Finance Code 001] & [Grant Number 88881.198822/2018-01]; Brazilian National Council for Scientific and Technological Development – CNPq [311757/2018-9]; Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro – FAPERJ [Grant number E-26/201.363/2021; E26/211.298/2021]

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Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

    Bruna Cristina Siqueira Kaiser, Renan Silva Santos, Rodrigo Goyannes Gusmão Caiado, Luiz Felipe Scavarda & Paulo Ivson Netto

  2. Federal Fluminense University (UFF), Niterói, Brazil

    Renan Silva Santos

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  1. Bruna Cristina Siqueira Kaiser
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  4. Luiz Felipe Scavarda
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Editor information

Editors and Affiliations

  1. Engineering, Universidad Anáhuac, Anáhuac Huixquilucan, Mexico

    Victor Manuel López Sánchez

  2. Mechanical Engineering, Federal University of Bahia, Salvador, Bahia, Brazil

    Francisco Gaudêncio Mendonça Freires

  3. Industrial Engineering and Management, Universidade Lusófona, Lisbon, Portugal

    João Carlos Gonçalves dos Reis

  4. DEGEIT, University of Aveiro, Aveiro, Portugal

    Joana Maria Costa Martins das Dores

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Kaiser, B.C.S., Santos, R.S., Caiado, R.G.G., Scavarda, L.F., Netto, P.I. (2022). Efficiency Assessment of Public Transport Vehicles Using Machine Learning and Non-parametric Models. In: López Sánchez, V.M., Mendonça Freires, F.G., Gonçalves dos Reis, J.C., Costa Martins das Dores, J.M. (eds) Industrial Engineering and Operations Management. IJCIEOM 2022. Springer Proceedings in Mathematics & Statistics, vol 400. Springer, Cham. https://doi.org/10.1007/978-3-031-14763-0_17

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