Infraestructura Vial ISSN Impreso: 1409-4045 ISSN electrónico: 2215-3705

OAI: https://revistas.ucr.ac.cr/index.php/vial/oai
Comparison in the application of classification methods to determine the mode of transportation of students to access the Rodrigo Facio campus of the Universidad Costa Rica in Montes de Oca, San Jose, Costa Rica
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Keywords

mode of transport
multivariate analysis
ensemble methods
classification methods
modo de transporte
análisis multivariado
métodos de ensamble
métodos de clasificación

How to Cite

Hernández Vega, H., & Sanabria Barboza, D. (2022). Comparison in the application of classification methods to determine the mode of transportation of students to access the Rodrigo Facio campus of the Universidad Costa Rica in Montes de Oca, San Jose, Costa Rica. Infraestructura Vial, 24(43), 1–10. https://doi.org/10.15517/iv.v24i43.48240

Abstract

This work presents the results of an exploratory process where different classification methods were applied to determine the mode of transportation for students to access the Rodrigo Facio campus of the University of Costa Rica. Among the analyzed models are binomial logistic regression, linear discriminant analysis, decision trees, K-closest neighbors, vector support machines and neural networks. A validation was carried out with the K-folds method and a precision higher than 83% was obtained for all the models analyzed. Similarly, the stacking assembly model was applied for the decision tree techniques, K-nearest neighbors, vector support machines, random forests, Bootstrap aggregation, binomial logistic regression, and the potentiation method, obtaining precision values higher than 86% in all cases. The random forest method gives the highest precision.

https://doi.org/10.15517/iv.v24i43.48240
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References

Alpízar, F., Piaggio, M., y Pacay, E. (2017). Valoración económica de los beneficios en la salud asociados a la reducción de la contaminación del aire. Santiago, Chile: CEPAL.

Bekhor, S., y Shiftan, Y. (2009). Specification and Estimation of Mode Choice Model Capturing Similarity between Mixed Auto and Transit Alternatives. Journal of Choice Modelling, 3(2), 29-49. DOI: 10.1016/S1755-5345(13)70034-4

Bjerre-Nielsen, A., Minor, K., Sapieżyński, P., Lehmann, S., y Lassen, D. D. (2020). Inferring transportation mode from smartphone sensors: Evaluating the potential of Wi-Fi and Bluetooth. PLOS ONE, 15(7), e0234003. DOI: 10.1371/journal.pone.0234003

Castro-Rodríguez, L, Picado-Aguilar, G., y Rodríguez-Shum, S. (2018). Evolución histórica de la modelación de demanda de transporte urbano en Costa Rica. Infraestructura Vial, 20, 4-47. DOI: 10.15517/iv.v20i1.33541

Dabiri, S., y Heaslip, K. (2018). Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies, 86, 360-371. DOI: 10.1016/j.trc.2017.11.021

Garber, N. y Hoel, L. (2005). Ingeniería de tránsito y carreteras. México, México D. F.: Thomson.

Hagenauer, J., y Helbich, M. (2017). A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, 273-282. DOI: 10.1016/j.eswa.2017.01.057

Hernández-Vega, H. y Umaña-Marín, G. (2018). Encuesta de Transporte 2018. Sede Rodrigo Facio Universidad de Costa Rica. Recuperado de: https://www.lanamme.ucr.ac.cr/repositorio/handle/50625112500/1582

Hillel, T., Bierlaire, M., Elshafie, M., y Jin, Y. (2020). A systematic review of machine learning classification methodologies for modelling passenger mode choice. Journal of Choice Modelling, 38, 100221. DOI: 10.1016/j.jocm.2020.100221

Hillel, T., Elshafie, M. Z., y Jin, Y. (2018). Recreating passenger mode choice-sets for transport simulation: A case study of London, UK. Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 171(1), 29-42. DOI: 10.1680/jsmic.17.00018

Jahangiri, A., y Rakha, H. (2014). Developing a support vector machine (SVM) classifier for transportation mode identification by using mobile phone sensor data. En Transportation Research Board 93rd Annual Meeting. Conferencia de Transportation Research Board, Washington D.C., Estados Unidos.

Jiménez-Serpa, J. C., Rojas-Sánchez, A. E., y Salas-Rondón, M. H. (2015). Tariff integration for public transportation in the metropolitan area of Bucaramanga. INGE CUC, 11(1), 25-33. DOI: 10.17981/ingecuc.11.1.2015.02

Jiménez-Serpa, J. C., y Salas-Rondón, M. H. (2016). Un caso de estudio sobre los factores que influyen para viajar en taxi compartido desde y hacia el aeropuerto. Ingeniería de Transporte, 20(01), 33-46.

Jiménez-Serpa, J. C., y Salas-Rondón, M. H. (2017). Aplicación de modelos econométricos para estimar la aceptabilidad de una tasa por congestión vehicular. INGE CUC, 13(2), 60-78. DOI: 10.17981/ingecuc.13.2.2017.08

Kaewwichian, P., Tanwanichkul, L., y Pitaksringkarn, J. (2019). Car ownership demand modeling using machine learning: Decision trees and neural networks. International Journal of GEOMATE, 17(62), 219-230. DOI: 10.21660/2019.62.94618

Ministerio de Ambiente y Energía (2015). VII Plan Nacional de Energía 2015-2030. Programa de las Naciones Unidas para el Desarrollo PNUD. Recuperado de: https://minae.go.cr/recursos/2015/pdf/VII-PNE.pdf

Muhsin Zambang, M. A., Jiang, H., y Wahab, L. (2021). Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana. PLOS ONE, 16(2), e0246044. DOI: 10.1371/journal.pone.0246044

Omrani, H. (2015). Predicting travel mode of individuals by machine learning. Transportation Research Procedia, 10, 840-849. DOI: 10.1016/j.trpro.2015.09.037

Ortúzar, J.D. (2012). Modelos de demanda de transporte. Santiago, Chile: Ediciones UC.

París-Bravo, D. (2019). Factores determinantes para la selección de modo auxiliar para acceder a Transmilenio (Tesis de grado). Universidad de los Andes, Colombia.

Programa Estado de la Nación (2019). Informe Estado de la Nación 2019. Recuperado de: https://estadonacion.or.cr/wpcontent/uploads/2019/11/informe_estado_nacion_2019.pdf

Sagaris, L., Mindell, J., Rojas-Rueda, D., Cortínez-O´Ryan, A., Sadrangani, K., Casanave-Macías, J., González-Sánchez, Y., y Hernández-Vega, H. (2021). Transporte, Salud, Equidad Acercamientos urgentes en un mundo con y post-Covid 19. Recuperado de: https://www.cambiarnos.cl/transporte-salud-y-equidad/

Salas-Rondón, M. H., Jiménez-Serpa, J. C., y Martínez-Estupiñán, Y. F. (2021). Subsidio a la tarifa para fortalecer la operación de los sistemas estratégicos de transporte público en Colombia. Revista UIS Ingenierías, 20(3), 77-90. DOI: 10.18273/revuin.v20n3-2021005

Sekhar, C. (2014). Mode Choice Analysis: The Data, the Models and Future Ahead. International Journal for Traffic & Transport Engineering, 4(3), 269 - 285. DOI: 10.7708/ijtte.2014.4(3).03

Sekhar, C. R., y Madhu, E. (2016). Mode choice analysis using random forrest decision trees. Transportation Research Procedia, 17, 644-652. DOI: 10.1016/j.trpro.2016.11.119

Souza Pitombo, C., Schindler Gomes Da Costa, A., y Salgueiro, A. R. (2015). Proposal of a sequential method for spatial interpolation of mode choice. Boletim de Ciências Geodésicas, 21(2), 274-289. DOI: 10.1590/S1982-21702015000200016

Vassilev, A. (2018). Data Mining Applied to Transportation Mode Classification Problem. Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), 36-46. DOI: 10.5220/0006633300360046

Zenina, N., y Borisov, A. (2011). Transportation Mode Choice Analysis Based on Classification Methods. Scientific Journal of Riga Technical University Computer Sciences, 45(1), 49-53. DOI: 10.2478/v10143-011-0041-2

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