Agent-Based Modelling for Evaluation of Transportation Mode Selection in the State of Guanajuato, Mexico

Authors

  • David Salas-Rodríguez Instituto Tepeyac de león
  • Luis Arturo Rivas-Tovar Instituto Politécnico Nacional

DOI:

https://doi.org/10.13053/cys-26-4-3989

Keywords:

Data-driven Modeling, Agent-based Simulation, Decision Tree Algorithm, Kappa Index, MCCI

Abstract

One of the negative consequences of the industrialization of Mexico favoured by the North American Free Trade Agreement (NAFTA), is the emergence of huge industrial corridors associated with the demand for mobility by commuters who move to their workplace. The demand produces mobility patterns that have a serious impact on air pollution in five cities in the state of Guanajuato that, despite being medium in size, outnumber Mexico City in pollution. The objective of this work is to model a data-driven agent based on the beliefs-desires-intentions model, to predict the selection of transport modes using a J48 decision tree algorithm that was designed from data from the 2015 national census (INEGI). The agent with system change function was programmed in Net logo.The results show that: it is possible to predict the demand of transport considering the: gender, level of education, transfer times and age in the five cities of Guanajuato, in a horizon of three years. With changes in public policies related to mobility and changes in transportation patterns, air pollution would be reduced. The proposed model could be used to support public policies that improve mobility and positively impact air quality in five cities in the state of Guanajuato.

Author Biographies

David Salas-Rodríguez, Instituto Tepeyac de león

Profesor del Instituto Tepeyac, director de investigación y postgrados. Línea de investigación: Complexity management, data science, predictive analytics and big data.Orcid ID: https://orcid.org/0000-0001-9144-7067 

Luis Arturo Rivas-Tovar, Instituto Politécnico Nacional

Catedrático del Instituto Politécnico Nacional. ESCA STO, Investigador Nacional Nivel III. Línea de investigación: Complexity management.Orcid ID: https://orcid.org/0000-0002-5186-9895

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Published

2022-12-25

Issue

Vol. 26 No. 4 (2022): 26(4) 2022

Section

Report on PhD Thesis

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