Desarrollo y validación de una metodología para la evaluación del desempeño ambiental del parque automotor liviano de Bogotá región a partir de ciclos típicos de conducción desarrollados por métodos estadísticos y de aprendizaje automático

Robayo Rueda, Daniel

dc.contributor.advisor	Barraza Botet, Cesar Luis
dc.contributor.advisor	Uribe Laverde, Miguel Angel
dc.contributor.author	Robayo Rueda, Daniel
dc.date.accessioned	2024-06-28T11:35:15Z
dc.date.available	2024-06-28T11:35:15Z
dc.date.issued	2024-02-13
dc.identifier.uri	http://hdl.handle.net/10818/60725
dc.description	165 páginas	es_CO
dc.description.abstract	Air pollution in recent years has led to serious illnesses in humans, such as premature deaths, cardiovascular diseases, and lung cancer (World Health Organization, 2018). In 2013, the WHO (World Health Organization), along with the IARC (International Agency for Research on Cancer), established that air pollution is carcinogenic to humans. Bogotá is one of the cities with larger levels of emissions, which has exceeded recommended levels for particulate matter smaller than 10 micrometers (PM10) since 1998 (Observatorio ambiental de Bogotá, 2022). While this indicator has been under control on an annual average since 2012, there are still high levels in certain sectors of the city (Observatorio Ambiental de Bogotá, 2022) compared to the limits set by Resolution 2254 of 2017 (Ministerio de ambiente y desarrollo sostenible, 2018).	en
dc.description.abstract	La contaminación del aire ha producido en los últimos años graves enfermedades en los seres humanos, tales como: muertes prematuras, enfermedades cardiovasculares y cáncer de pulmón (World Health Organization, 2018). En el año 2013, la OMS (Organización Mundial de la Salud) junto con la IARC (World Health Organization Internacional Agency for Research on cancer), establecieron que la contaminación del aire es cancerígena para los seres humanos. Bogotá es una de la ciudades donde se presenta contaminación del aire, la cual desde el año 1998 excedió los niveles recomendados para el material particulado menor a 10 micrómetros (PM10)(Observatorio ambiental de Bogotá, 2022), controlando este indicador desde el año 2012 en promedio anual, pero mostrando altos índices para algunos sectores de la ciudad (Observatorio Ambiental de Bogotá, 2022) en comparación a los límites establecidos por la resolución 2254 del 2017 (Ministerio de ambiente y desarrollo sostenible, 2018).	es_CO
dc.format	application/pdf	es_CO
dc.language.iso	spa	es_CO
dc.publisher	Universidad de La Sabana	es_CO
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject.other	Analizador de gases
dc.subject.other	Ciclo típico de conducción
dc.subject.other	Dinamómetro de chasis
dc.subject.other	Factores de emisión IVDR
dc.subject.other	k-means
dc.subject.other	Metaheurística
dc.title	Desarrollo y validación de una metodología para la evaluación del desempeño ambiental del parque automotor liviano de Bogotá región a partir de ciclos típicos de conducción desarrollados por métodos estadísticos y de aprendizaje automático	es_CO
dc.type	master thesis	es_CO
dc.type.hasVersion	publishedVersion	es_CO
dc.rights.accessRights	openAccess	es_CO
dcterms.references	3DATX. (2023). par SYNC FLEX PNC Brochure. https://3datx.com/wpcontent/uploads/3DATX_parSYNC_FLEX-PNC_brochure_EU_06-02-23_v2.pdf
dcterms.references	Alves, C. A., Lopes, D. J., Calvo, A. I., Evtyugina, M., Rocha, S., & Nunes, T. (2015). Emissions from light-duty diesel and gasoline in-use vehicles measured on chassis dynamometer test cycles. Aerosol and Air Quality Research, 15(1), 99–116. https://doi.org/10.4209/aaqr.2014.01.0006
dcterms.references	Berthold, M. R., Lenz, H.-J., Bradley, E., Kruse, R., & Borgelt, C. (2003). LNCS 2810 - Advances in Intelligent Data Analysis V.
dcterms.references	Cano, J. R., Herrera, F., Sanchez, L., Cano, J. R., Cordón, O., Herrera, F., & Sánchez, L. (2002). A greedy randomized adaptive search procedure applied to the clusterin gproblem as an initialization process using K-Means as a local search procedure. Journal of Intelligent & Fuzzy Systems, 12, 235–242. https://www.researchgate.net/publication/220256785
dcterms.references	Chauhan, B., Pithawala, C. K., Joshi, G., Vallabhbhai, S., Chauhan, B. P., Joshi, G. J., & Parida, P. (2020). Development of Candidate Driving Cycles for an Urban Arterial Corridor of Vadodara City. https://www.researchgate.net/publication/348252664
dcterms.references	Clarkson, D., & Middleton, J. T. (1962). The california control program for motor vehicle created air pollution. Journal of the Air Pollution Control Association, 12(1), 22–28. https://doi.org/10.1080/00022470.1962.10468042
dcterms.references	DANE. (2019). Estructura de población : Bogotá D . C ., CG 2005 y CNPV 2018 Hombres Mujeres. https://sitios.dane.gov.co/cnpv/app/views/informacion/fichas/11.pdf
dcterms.references	Desineedi, R. M., Mahesh, S., & Ramadurai, G. (2020). Developing driving cycles using k-means clustering and determining their optimal duration. Transportation Research Procedia, 48(2018), 2083–2095. https://doi.org/10.1016/j.trpro.2020.08.268
dcterms.references	Division, C., & Agency, U. S. E. P. (1993). Federal Test Procedure Review Project : Preliminary Technical Report May 1993. Environmental Protection, May
dcterms.references	Doenhoff, von, Marks II, R. J., Choi, J. J., Healy, M., Tsao, C. K., Bezdek, J. C., Pal, N. R., Kohonen, F., Krishna, K., & Narasimha Murty, M. (1999). Dynamic parameter encoding for genetic algorithms. In Pattern Recognit (Vol. 29, Issue 3). Morgan Kauffman.
dcterms.references	Duran, A., & Earleywine, M. (2012). GPS data filtration method for drive cycle analysis applications. SAE Technical Papers. https://doi.org/10.4271/2012-01-0743
dcterms.references	Echeverry Mejía, J. M. (2018). Metodología para reducir el gasto de combustible en rutas fijas mediante el uso de hábitos de conducción eficiente, empleando un sistema IVDR y ciclos de conducción.
dcterms.references	Echeverry-Mejía, J., Arenas-Uribe, F., Contreras, D., & Vásquez, V. (2022). Design and Validation of an In-Vehicle Data Recorder System for Testing Purposes. IEEE Latin America Transactions, 100(XXX). https://latamt.ieeer9.org/index.php/transactions/article/view/6489/1654
dcterms.references	Fotouhi, A., & Montazeri-Gh, M. (2013). Tehran driving cycle development using the k-means clustering method. Scientia Iranica, 20(2), 286–293. https://doi.org/10.1016/j.scient.2013.04.001
dcterms.references	Freescale. (2014). KEA128 Sub-Family Reference Manual
dcterms.references	Frey, C., & Eichenberger, D. (1997). Remote Sensing of Mobile Source Air Pollutant Emissions : Variability and Uncertainty in On-Road Emissions Estimates of Carbon Monoxide and Hydrocarbons for School and Transit Buses. June, 168.
dcterms.references	Giakoumis, E. G. (2016). Driving and engine cycles. In Driving and Engine Cycles. Springer International Publishing. https://doi.org/10.1007/978-3-319-49034-2
dcterms.references	Gitelman, V., Bekhor, S., Doveh, E., Pesahov, F., Carmel, R., & Morik, S. (2018). Exploring relationships between driving events identified by in-vehicle data recorders, infrastructure characteristics and road crashes. Transportation Research Part C: Emerging Technologies, 91(April), 156–175. https://doi.org/10.1016/j.trc.2018.04.003
dcterms.references	Hass, G. C., & Brubacher, M. L. (1962). A test procedure for motor vehicle exhaust emissions. Journal of the Air Pollution Control Association, 12(11), 505–543. https://doi.org/10.1080/00022470.1962.10468120
dcterms.references	Ho, S. H., Wong, Y. D., & Chang, V. W. C. (2014). Developing Singapore Driving Cycle for passenger cars to estimate fuel consumption and vehicular emissions. Atmospheric Environment, 97, 353–362. https://doi.org/10.1016/j.atmosenv.2014.08.042
dcterms.references	Huertas, J. I. (2017). A new methodology to determine typical driving cycles for the design of vehicles power trains. 12008. https://doi.org/10.1007/s12008-017-0379-y
dcterms.references	Huertas, J. I., Díaz, J., Cordero, D., & Cedillo, K. (2018). A new methodology to determine typical driving cycles for the design of vehicles power trains. International Journal on Interactive Design and Manufacturing, 12(1), 319–326. https://doi.org/10.1007/s12008-017-0379-y
dcterms.references	Huertas, J. I., Quirama, L. F., Giraldo, M., & Díaz, J. (2019). Comparison of three methods for constructing real driving cycles. Energies, 12(4). https://doi.org/10.3390/en12040665
dcterms.references	ICONTEC. (2012). Ntc 4983. Icontec, 571, 31.
dcterms.references	International Agency for Research on Cancer. (2013). IARC: Outdoor air pollution a leading environmental cause of cancer deaths. 221, 1–4. https://doi.org/10.1002/em
dcterms.references	International Organization for Standardization (ISO). (2006). Road vehicles — Controller area network ( CAN ) — Part 1 : Data link layer and physical signalling. 2006, 1–6.
dcterms.references	Jia, X., Wang, H., Xu, L., Wang, Q., Li, H., Hu, Z., Li, J., & Ouyang, M. (2021). Constructing representative driving cycle for heavy duty vehicle based on Markov chain method considering road slope. Energy and AI, 6, 100115. https://doi.org/10.1016/J.EGYAI.2021.100115
dcterms.references	Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35(1), 1–19. https://doi.org/10.1207/S15327906MBR3501_1
dcterms.references	Jun, G., Guensler, R., & Ogle, J. H. (2006). Smoothing methods to minimize impact of global positioning system random error on travel distance, speed, and acceleration profile estimates. Transportation Research Record, 1972, 141–150. https://doi.org/10.3141/1972-19
dcterms.references	Khadse, V. M., Mahalle, P. N., & Shinde, G. R. (2020). Statistical study of machine learning algorithms using parametric and non-parametric tests: A comparative analysis and recommendations. International Journal of Ambient Computing and Intelligence, 11(3), 80–105. https://doi.org/10.4018/IJACI.2020070105
dcterms.references	Kharrazi, S., Almen, M., Frisk, E., Nielsen, L., Kharrazi, S., Almén, M., Frisk, E., & Nielsen, L. (2019). Extending Behavioral Models to Generate Mission-Based Driving Cycles for Data-Driven Vehicle Development Extending behavioral models to generate mission-based driving cycles for data-driven vehicle development.
dcterms.references	Kim, Y., & Bang, H. (n.d.). Introduction to Kalman Filter and Its Applications. www.intechopen.com
dcterms.references	Lin, J., & Niemeier, D. A. (2002). An exploratory analysis comparing a stochastic driving cycle to California’s regulatory cycle. Atmospheric Environment, 36(38), 5759–5770. https://doi.org/10.1016/S1352-2310(02)00695-7
dcterms.references	Liu, J., Wang, X., & Khattak, A. (2016). Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users. Transportation Research Part C: Emerging Technologies, 67, 280–298. https://doi.org/10.1016/j.trc.2016.02.016
dcterms.references	Liu, Y., Xin Wu, Z., Zhou, H., Zheng Nan Yu, H., Pan An, X., Yuan Li, J., & Liang Li, M. (2020). DEVELOPMENT OF CHINA LIGHT-DUTY VEHICLE TEST CYCLE. International Journal of Automotive Technology, 21(5), 1233–1246. https://doi.org/10.1007/s12239−020−0117−5
dcterms.references	Lundby, K. M., & Johnson, J. W. (2006). Relative Weights of Predictors What Is Important When Many Forces Are Operating.
dcterms.references	Ministerio de Ambiente y Desarrollo Sostenible. (2017). Guía para la elaboración de Inventarios de Emisiones Atmosféricas. http://www.minambiente.gov.co/images/AsuntosambientalesySectorialyUrbana/pdf/emisiones_atmo sfericas_contaminantes/documentos_relacionados/GUIA_PARA_LA_ELABORACION_DE_INVE NTARIOS_DE_EMISIONES_ATMOSFERICAS.pdf
dcterms.references	Ministerio de ambiente y desarrollo sostenible. (2018). Resolución 2254 de 2017. https://www.icbf.gov.co/cargues/avance/docs/resolucion_minambienteds_2254_2017.htm
dcterms.references	Ministro Ambiente Y Desarrollo Sostenible. (2022). Resolución 0762 del 18 de Julio de 2022. https://www.minambiente.gov.co/wp-content/uploads/2022/09/Resolucion-762-de-2022.pdf
dcterms.references	Moradi, E., & Miranda-Moreno, L. (2020). Vehicular fuel consumption estimation using real-world measures through cascaded machine learning modeling. Transportation Research Part D: Transport and Environment, 88. https://doi.org/10.1016/j.trd.2020.102576
dcterms.references	NEO-6 u-blox 6 GPS Modules Data Sheet. (2011). www.u-blox.com
dcterms.references	Nguyen, Y.-L. T., Bui, N. D., Nghiem, T.-D., & Le, A.-T. (2020). GPS DATA PROCESSING FOR DRIVING CYCLE DEVELOPMENT IN HANOI, VIETNAM. In Journal of Engineering Science and Technology (Vol. 15, Issue 2)
dcterms.references	Nouri, P., & Morency, C. (2017). Evaluating microtrip definitions for developing driving cycles. Transportation Research Record, 2627, 86–92. https://doi.org/10.3141/2627-10
dcterms.references	Nyberg, P., Frisk, E., & Nielsen, L. (2014). Generation of equivalent driving cycles using Markov chains and mean tractive force components. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 19, Issue 3). IFAC. https://doi.org/10.3182/20140824-6-za-1003.02239
dcterms.references	Observatorio Ambiental de Bogotá. (2022). Cifras e Indicadores de Medio Ambiente en Bogotá Concentración de Material Particulado Inferior a 10 Micrómetros {PM10} Promedio Anual por Estación - PM10PAE. https://oab.ambientebogota.gov.co/indicadores/?id=24341000-a6e7-11ebb6f5-a915ff441b6d
dcterms.references	Observatorio ambiental de Bogotá. (2022). Cifras e Indicadores de Medio Ambiente en Bogotá Material Particulado Inferior a 10 Micras {µ} Promedio Anual - PM10. https://oab.ambientebogota.gov.co/indicadores/?id=d2ccd170-0178-11ea-8cc7-8197075aabad
dcterms.references	Onnegren, P. (2013). Driving cycle generation using statistical analysis and markov chains. Department of Electrical Engineering, LiTH-ISY-EX-13/4670--SE, 89.
dcterms.references	Pavlovic, J., Ciuffo, B., Fontaras, G., Valverde, V., & Marotta, A. (2018). How much difference in typeapproval CO2 emissions from passenger cars in Europe can be expected from changing to the new test procedure (NEDC vs. WLTP)? Transportation Research Part A: Policy and Practice, 111(February), 136–147. https://doi.org/10.1016/j.tra.2018.02.002
dcterms.references	Peña, J. M., Lozano, J. A., & Larrañaga, P. (1999). An empirical comparison of four initialization methods for the K-Means algorithm. www.elsevier.nl/locate/patrec
dcterms.references	Peng, Y., Zhuang, Y., & Yang, Y. (2020). A driving cycle construction methodology combining k-means clustering and Markov model for urban mixed roads. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234(2–3), 714–724. https://doi.org/10.1177/0954407019848873
dcterms.references	Qiu, D., Li, Y., & Qiao, D. (2018). Recurrent Neural Network Based Driving Cycle Development for Light Duty Vehicles in Beijing. Transportation Research Procedia, 34, 147–154. https://doi.org/10.1016/j.trpro.2018.11.026
dcterms.references	R Core Team. (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
dcterms.references	Roy, F., & Morency, C. (2020). Comparing Driving Cycle Development Methods Based on Markov Chains. Transportation Research Record, 2675(3), 212–221. https://doi.org/10.1177/0361198120968829
dcterms.references	Secretaría de Ambiente de Bogotá. (2022). Inventario de emisiones contaminantes atmosféricos 2020. https://drive.google.com/file/d/1a8gyjx0h0OPa6Apx8J9h-9lgfiKN-Df-/view
dcterms.references	Selesnick, I. (2022). Least Squares with Examples in Signal Processing. http://eeweb.poly.edu/iselesni/lecture_notes/
dcterms.references	Shi, S., Lin, N., Zhang, Y., Cheng, J., Huang, C., Liu, L., & Lu, B. (2016). Research on Markov property analysis of driving cycles and its application. Transportation Research Part D: Transport and Environment, 47, 171–181. https://doi.org/10.1016/j.trd.2016.05.013
dcterms.references	Society of Automotive Engineers. (2007). SAE J1979.
dcterms.references	Society of Automotive Engineers. (2016). SAE J1962 - Diagnostic Connector. In SAE Standards.
dcterms.references	Society of Automotive Engineers (SAE). (2010). SAE J1263 - Road load measurement and dynamometer simulation using coastdown techniques. In SAE Technical Papers. https://doi.org/10.4271/810828
dcterms.references	Society of Automotive Engineers (SAE). (2011). SAE J254 - Instrumentation and techniques for exhaust gas emissions measurement. In SAE Standards. https://www.sae.org/standards/content/j254_201106/
dcterms.references	Society of Automotive Engineers (SAE). (2014). SAE J2951 - Drive Quality Evaluation for Chassis Dynamometer Testing: Surface Vehicle Recommended Practice. In SURFACE VEHICLE RECOMMENDED PRACTICE: Vol. SAE J2951. https://www.sae.org/standards/content/j2951_201401/
dcterms.references	The Mathworks Inc. (2022a). MATLAB (2022a). The Mathworks Inc.
dcterms.references	The Mathworks Inc. (2022b). Statistics and Machine Learning Toolbox (2022a). Mathworks Inc.
dcterms.references	The National Renewable Energy Laboratory. (n.d.). DriveCAT: Drive Cycle Analysis Tool. DriveCAT: Drive Cycle Analysis Tool. Retrieved October 12, 2022, from https://www.nrel.gov/transportation/drive-cycle-tool/
dcterms.references	The National Renewable Energy Laboratory (NREL). (2015). Transportation Secure Data Center - NREL. Drive Cycle Data. www.nrel.gov/tsdc.
dcterms.references	Toledo, T., Musicant, O., & Lotan, T. (2008). In-vehicle data recorders for monitoring and feedback on drivers’ behavior. Transportation Research Part C: Emerging Technologies, 16(3), 320–331. https://doi.org/10.1016/j.trc.2008.01.001
dcterms.references	Tutuianu, M., Bonnel, P., Ciuffo, B., Haniu, T., Ichikawa, N., Marotta, A., Pavlovic, J., & Steven, H. (2015). Development of the World-wide harmonized Light duty Test Cycle (WLTC) and a possible pathway for its introduction in the European legislation. Transportation Research Part D: Transport and Environment, 40, 61–75. https://doi.org/10.1016/j.trd.2015.07.011
dcterms.references	USEPA AP 42. (1995). AP 42, Fifth Edition Compilation of Air Pollutant Emission Factors, Volume 1: Stationary and Point Sources. AP 42, Fifth Edition Compilation of Air Pollutant Emission Factors, Volume 1: Stationary and Point Sources, 1–10. https://www3.epa.gov/ttnchie1/ap42/c00s00.pdf%0Ahttps://www3.epa.gov/ttn/chief/ap42/c00s00.pd f
dcterms.references	World Health Organization. (2018). Ambient (outdoor) air pollution. Https://Www.Who.Int/En/NewsRoom/Fact-Sheets/Detail/Ambient-(Outdoor)-Air-Quality-and-Health. https://www.who.int/newsroom/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
dcterms.references	World Health Organization. (2021a). WHO global air quality guidelines.
dcterms.references	World Health Organization. (2021b). WHO Global Air Quality Guidelines.
dcterms.references	Wu, Y., Zhang, W., Zhang, L., Qiao, Y., Yang, J., & Cheng, C. (2020). A multi-clustering algorithm to solve driving cycle prediction problems based on unbalanced data sets: A Chinese case study. Sensors (Switzerland), 20(9). https://doi.org/10.3390/s20092448
dcterms.references	Yang, Y., Li, T., Hu, H., Zhang, T., Cai, X., Chen, S., & Qiao, F. (2019). Development and emissions performance analysis of local driving cycle for small-sized passenger cars in Nanjing, China. Atmospheric Pollution Research, 10(5), 1514–1523. https://doi.org/10.1016/j.apr.2019.04.009
dcterms.references	Yuan, M., Kan, X., Chi, C. H., Cao, L., Shu, H., Fan, Y., & Yao, W. (2021). Study of Driving Cycle of City Tour Bus Based on Coupled GA-K-Means and HMM Algorithms: A Case Study in Beijing. IEEE Access, 9, 20331–20345. https://doi.org/10.1109/ACCESS.2021.3054118
dcterms.references	Yuhui, P., Yuan, Z., & Huibao, Y. (2019). Development of a representative driving cycle for urban buses based on the K-means cluster method. Cluster Computing, 22(s3), 6871–6880. https://doi.org/10.1007/s10586-017-1673-y
dcterms.references	Zhang, C., Kotz, A., Kelly, K., & Rippelmeyer, L. (2021). Development of heavy-duty vehicle representative driving cycles via decision tree regression. Transportation Research Part D: Transport and Environment, 95(May), 102843. https://doi.org/10.1016/j.trd.2021.102843
thesis.degree.discipline	Facultad de Ingeniería	es_CO
thesis.degree.level	Maestría en Diseño y Gestión de Procesos	es_CO
thesis.degree.name	Magíster en Diseño y Gestión de Procesos	es_CO