Análisis de la demanda actual y potencial, factores motivadores y barreras, para la implementación de servicios de carpooling en comunidades universitarias de Bogotá

dc.contributor.advisorMangones Matos, Sonia Ceciliaspa
dc.contributor.advisorOrozco Fontalvo, Mauriciospa
dc.contributor.authorMerchán Núñez, César Augustospa
dc.contributor.cvlacMerchán Núñez, César Augusto [0002274101]
dc.contributor.orcidMerchán Núñez, César Augusto [0009-0000-0821-8133]
dc.contributor.researchgroupGrupo de Investigación en Logística para El Transporte Sostenible y la Seguridad Translogytspa
dc.coverage.cityBogotáspa
dc.coverage.countryColombiaspa
dc.coverage.regionCundinamarcaspa
dc.coverage.tgnhttp://vocab.getty.edu/page/tgn/1000838
dc.date.accessioned2025-08-27T19:22:13Z
dc.date.available2025-08-27T19:22:13Z
dc.date.issued2025-08-26
dc.descriptionilustraciones, diagramas, fotografíasspa
dc.description.abstractEl carpooling, como alternativa de transporte sostenible, ofrece beneficios reconocidos globalmente, como la reducción del consumo energético, las emisiones contaminantes y la congestión vehicular. Aunque su adopción en ciudades del Sur Global enfrenta barreras económicas, tecnológicas y sociales, en lugares como Bogotá —con alta densidad urbana, tráfico congestionado y niveles elevados de contaminación— esta opción podría mejorar significativamente la movilidad y la calidad de vida. A pesar de diversas iniciativas y políticas locales, la implementación de servicios de carpooling sigue siendo limitada, especialmente en comunidades universitarias, donde los factores que influyen en su adopción aún no han sido investigados de manera adecuada. Este estudio busca llenar este vacío mediante una encuesta aplicada a 470 miembros de la comunidad universitaria en Bogotá, utilizando un experimento de elección discreta (DCE por sus siglas en ingles) y modelos estadísticos avanzados como el análisis de correspondencia múltiple, modelos logit y aprendizaje automático. Los resultados revelan que los principales factores que determinan la adopción del carpooling incluyen la sensibilidad al precio, las percepciones de seguridad—particularmente en relación con el género—la preferencia por compartir viajes con compañeros conocidos y características demográficas como la edad y el nivel socioeconómico. Con base en estos resultados, este estudio no solo contribuye a la comprensión de las dinámicas de movilidad compartida en contextos urbanos complejos, sino que también proporciona información valiosa para el diseño de políticas públicas y estrategias de implementación de servicios de carpooling, adaptadas a las condiciones locales de Bogotá y otras ciudades similares del Sur Global. (Texto tomado de la fuente).spa
dc.description.abstractCarpooling, as a sustainable transportation alternative, offers globally recognized benefits, including reduced energy consumption, lower pollutant emissions, and decreased traffic congestion. However, its adoption in cities of the Global South faces economic, technological, and social barriers. In Bogotá—characterized by high urban density, heavy traffic congestion, and severe pollution—carpooling has the potential to significantly improve mobility and quality of life. Despite various local initiatives and policies, the implementation of carpooling services remains limited, particularly within university communities, where the factors influencing adoption have yet to be adequately studied. This study addresses this gap through a survey of 470 university community members in Bogotá, employing a discrete choice experiment (DCE) alongside advanced statistical models such as multiple correspondence analysis, logit models, and machine learning. The results indicate that key factors influencing carpooling adoption include price sensitivity, safety perceptions—particularly regarding gender—preference for sharing rides with known companions, and demographic characteristics such as age and socioeconomic status. These findings contribute to a deeper understanding of shared mobility dynamics in complex urban contexts and offer valuable insights for designing public policies and implementing carpooling services tailored to Bogotá and other similar cities in the Global South.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Transportespa
dc.description.methodsEnfoque metodológico dividido en cuatro secciones: Tamaño de la muestra y características sociodemográficas; diseño de la encuesta; marco de modelado de elección discreta y marco de modelado de aprendizaje automático. Bogotá (Colombia) fue nuestra área de estudio. La población estuvo compuesta por estudiantes y profesores de instituciones de educación superior públicas y privadas reconocidas por el Ministerio de Educación Nacional de Colombia. La información se obtuvo del Sistema Nacional de Información de Educación Superior – SNIES (MinEducación, 2023), que reporta 344.469 estudiantes matriculados y 41.065 profesores en instituciones acreditadas para el segundo semestre de 2023spa
dc.description.researchareaTransporte y medio ambientespa
dc.description.technicalinfoEnfoque metodológico dividido en cuatro secciones: Tamaño de la muestra y características sociodemográficas; diseño de la encuesta; marco de modelado de elección discreta y marco de modelado de aprendizaje automático. Bogotá (Colombia) fue nuestra área de estudio. La población estuvo compuesta por estudiantes y profesores de instituciones de educación superior públicas y privadas reconocidas por el Ministerio de Educación Nacional de Colombia. La información se obtuvo del Sistema Nacional de Información de Educación Superior – SNIES (MinEducación, 2023), que reporta 344.469 estudiantes matriculados y 41.065 profesores en instituciones acreditadas para el segundo semestre de 2023.spa
dc.format.extent140 páginas +1 anexospa
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/88490
dc.language.isospa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Ingeniería Civil y Agrícolaspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Transportespa
dc.relation.referencesAbrahamse, W., & Keall, M. (2012a). Effectiveness of a web-based intervention to encourage carpooling to work: A case study of Wellington, New Zealand. Transport Policy, 21, 45–51. https://doi.org/10.1016/J.TRANPOL.2012.01.005spa
dc.relation.referencesAgatz, N., Erera, A., Savelsbergh, M., & Wang, X. (2012). Optimization for dynamic ride-sharing: A review. European Journal of Operational Research, 223(2), 295–303. https://doi.org/10.1016/j.ejor.2012.05.028spa
dc.relation.referencesAgresti, Alan. (2018). Statistical methods for the social sciences. Pearson.spa
dc.relation.referencesAmey, A. M., Attanucci, J., & Mishalani, R. (2011). “Real-Time” Ridesharing-The Opportunities and Challenges of Utilizing Mobile Phone Technology to Improve Rideshare Services. Transportation Research Record, 2217(1), 103–110.spa
dc.relation.referencesANSV. (2024). Cifras año en curso. https://ansv.gov.co/es/observatorio/estadísticas/cifras-ano-en-cursospa
dc.relation.referencesArias-Molinares, D., & García-Palomares, J. C. (2020). The Ws of MaaS: Understanding mobility as a service fromaliterature review. In IATSS Research (Vol. 44, Issue 3, pp. 253–263). Elsevier B.V. https://doi.org/10.1016/j.iatssr.2020.02.001spa
dc.relation.referencesAshraf Javid, M., & Al-Khayyat, M. A. (2021). Factors affecting the student’s intentions to choose carpooling: a case study in Oman. Journal of the Chinese Institute of Engineers, 44(4), 332–341. https://doi.org/10.1080/02533839.2021.1897685spa
dc.relation.referencesBanco Mundial. (2020). Informe Anual 2020. https://doi.org/10.1596/978-1-4648-1623-9spa
dc.relation.referencesBastos, J. T., dos Santos, P. A. B., Amancio, E. C., Gadda, T. M. C., Ramalho, J. A., King, M. J., & Oviedo-Trespalacios, O. (2021). Is organized carpooling safer? Speeding and distracted driving behaviors from a naturalistic driving study in Brazil. Accident Analysis & Prevention, 152, 105992. https://doi.org/10.1016/J.AAP.2021.105992spa
dc.relation.referencesBen-Akiva, M., & Lerman, S. (1985). Discrete choice analysis: theory and application to travel demand. https://books.google.com/books?hl=es&lr=&id=oLC6ZYPs9UoC&oi=fnd&pg=PR14&ots=nPcxjZcmJf&sig=3Rgqq8X_w-J10JOapoYoxjxbSMospa
dc.relation.referencesBenzécri, J.-P. (1992). Correspondence analysis handbook. New York : Marcel Dekker.spa
dc.relation.referencesBergstra, J., Ca, J. B., & Ca, Y. B. (2012). Random Search for Hyper-Parameter Optimization Yoshua Bengio. Journal of Machine Learning Research, 13, 281–305. http://scikit-learn.sourceforge.net.spa
dc.relation.referencesBID, & SDM. (2022). CONSULTORÍA PARA EL DESARROLLO DEL MODELO DE ELECCIÓN MODAL PARA EVALUAR EL IMPACTO DE LAS MEDIDAS DE GESTIÓN DE LA DEMANDA DE VEHÍCULOS PARTICULARES EN BOGOTÁ.spa
dc.relation.referencesBishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. https://doi.org/10.1093/oso/9780198538493.001.0001spa
dc.relation.referencesBishop, C. M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag.spa
dc.relation.referencesBreiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324spa
dc.relation.referencesBuliung, R. N., Soltys, K., Habel, C., & Lanyon, R. (2009). Driving Factors behind Successful Carpool Formation and Use. Transportation Research Record, 2118(1), 31–38. https://doi.org/10.3141/2118-05spa
dc.relation.referencesBulteau, J., Feuillet, T., & Dantan, S. (2019). Carpooling and carsharing for commuting in the Paris region: A comprehensive exploration of the individual and contextual correlates of their uses. Travel Behaviour and Society, 16, 77–87. https://doi.org/10.1016/j.tbs.2019.04.007spa
dc.relation.referencesBurnham, K. P., & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304. https://doi.org/10.1177/0049124104268644spa
dc.relation.referencesCantwell, M., Caulfield, B., & O’Mahony, M. (2009). Examining the Factors that Impact Public Transport Commuting Satisfaction. Journal of Public Transportation, 12(2), 1–21. https://doi.org/10.5038/2375-0901.12.2.1spa
dc.relation.referencesCellina, F., Derboni, M., Giuffrida, V., Tomic, U., & Hoerler, R. (2024a). Trust me if you can: Practical challenges affecting the integration of carpooling in Mobility-as-a-Service platforms. Travel Behaviour and Society, 37, 100832. https://doi.org/10.1016/J.TBS.2024.100832spa
dc.relation.referencesChan, N. D., & Shaheen, S. A. (2012). Ridesharing in North America: Past, Present, and Future. In Transport Reviews (Vol. 32, Issue 1, pp. 93–112). https://doi.org/10.1080/01441647.2011.621557spa
dc.relation.referencesChaube, Vineeta., Kavanaugh, A. L., & Pérez-Quiñones, M. A. (2010). Leveraging Social Networks to Embed Trust in Rideshare Programs. Proceedings of the 43rd Hawaii International Conference on System Sciences.spa
dc.relation.referencesCiari, F., & Axhausen, K. (2012). Choosing carpooling or car sharing as a mode Swiss stated choice experiments. Transportation Research Board . https://doi.org/10.3929/ethz-b-000091515spa
dc.relation.referencesCiasullo, M. V., Troisi, O., Loia, F., & Maione, G. (2018). Carpooling: travelers’ perceptions from a big data analysis. The TQM Journal, 30(5), 554–571. https://doi.org/10.1108/TQM-11-2017-0156spa
dc.relation.referencesCorreia, G. H., Silva, J. A., & Viegas, J. M. (2013). Using latent attitudinal variables estimated through a structural equations model for understanding carpooling propensity. Transportation Planning and Technology, 36(6), 499–519. https://doi.org/10.1080/03081060.2013.830894spa
dc.relation.referencesCox, D. R., & Snell, E. J. (1990). Analysis of Binary Data. Biometrics, 46(2). https://doi.org/10.2307/2531476spa
dc.relation.referencesCrozet, Y., Santos, G., & Coldefy, J. (2019). Shared mobility, MaaS and the regulatory challenges of urban mobility. Center on Regulation in Europe (CERRE). www.cerre.euspa
dc.relation.referencesDe la Fuente, S. (2020). REGRESIÓN LOGÍSTICA.spa
dc.relation.referencesDeakin, E., Frick, K. T., & Shively, K. M. (2010). Markets for dynamic ridesharing? Transportation Research Record, 2187, 131–137. https://doi.org/10.3141/2187-17spa
dc.relation.referencesDean, A., Voss, D., & Draguljić, D. (2017). Planning Experiments. In A. Dean, D. Voss, & D. Draguljić (Eds.), Design and Analysis of Experiments (pp. 7–30). Springer International Publishing. https://doi.org/10.1007/978-3-319-52250-0_2spa
dc.relation.referencesDelhomme, P., & Gheorghiu, A. (2016). Comparing French carpoolers and non-carpoolers: Which factors contribute the most to carpooling? Transportation Research Part D: Transport and Environment, 42, 1–15. https://doi.org/10.1016/j.trd.2015.10.014spa
dc.relation.referencesDorner, F., & Berger, M. (2016). Community-based mobility: a transport option for rural areas? Transport Research Arena TRA 2018, 7. https://www.researchgate.net/publication/330398168spa
dc.relation.referencesDubernet, T. J. P., Rieser-Schüssler, N., & Axhausen, K. W. (2013). Using a multi-agent simulation tool to estimate the car-pooling potential Conference Paper. ETH Zurich Research Collection. https://doi.org/10.3929/ethz-b-000052402spa
dc.relation.referencesEuropean Commission’s transport. (1998). Quality Approach in Tendering Urban Public Transport Operations | TRIMIS. https://trimis.ec.europa.eu/project/quality-approach-tendering-urban-public-transport-operationsspa
dc.relation.referencesFleiter, J., Lennon, A., & Watson, B. (2010). How do other people influence your driving speed? Exploring the “who” and “how” of social influences on speeding from a qualitative perspective. Transportation Research Part F: Traffic Psychology and Behaviour, 13, 49–62. https://doi.org/10.1016/j.trf.2009.10.002spa
dc.relation.referencesFox, J. (2008). Applied regression analysis and generalized linear models.spa
dc.relation.referencesFuruhata, M., Dessouky, M., Ordóñez, F., Brunet, M. E., Wang, X., & Koenig, S. (2013a). Ridesharing: The state-of-the-art and future directions. Transportation Research Part B: Methodological, 57, 28–46. https://doi.org/10.1016/j.trb.2013.08.012spa
dc.relation.referencesGargiulo, E., Giannantonio, R., Guercio, E., Borean, C., & Zenezini, G. (2015). Dynamic Ride Sharing Service: Are Users Ready to Adopt it? Procedia Manufacturing, 3, 777–784. https://doi.org/10.1016/j.promfg.2015.07.329spa
dc.relation.referencesGeels, F. W. (2012). A socio-technical analysis of low-carbon transitions: introducing the multi-level perspective into transport studies. Journal of Transport Geography, 24, 471–482. https://doi.org/10.1016/J.JTRANGEO.2012.01.021spa
dc.relation.referencesGheorghiu, A., & Delhomme, P. (2018). For which types of trips do French drivers carpool? Motivations underlying carpooling for different types of trips. Transportation Research Part A: Policy and Practice, 113, 460–475. https://doi.org/10.1016/j.tra.2018.05.002spa
dc.relation.referencesGreenacre, M. (2017). Correspondence Analysis in Practice (3rd ed.). CRC Press. https://doi.org/https://doi.org/10.1201/9781315369983spa
dc.relation.referencesGreene, W. H. . (2012). Econometric analysis (PEARSON, Ed.). Prentice Hall.spa
dc.relation.referencesGreene, W. H., & Hensher, D. A. (2010). Modeling ordered choices: A primer. In Modeling Ordered Choices: A Primer. Cambridge University Press. https://doi.org/10.1017/CBO9780511845062spa
dc.relation.referencesGuidotti, R., Nanni, M., Rinzivillo, S., Pedreschi, D., & Giannotti, F. (2017). Never drive alone: Boosting carpooling with network analysis. Information Systems, 64, 237–257. https://doi.org/10.1016/j.is.2016.03.006spa
dc.relation.referencesGuo, Y., Simpson, J. R., & Pignatiello, J. J. (2009). The general balance metric for mixed‐level fractional factorial designs. Quality and Reliability Engineering International, 25. https://api.semanticscholar.org/CorpusID:42202801spa
dc.relation.referencesGurumurthy, K. M., & Kockelman, K. M. (2020). Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices. Technological Forecasting and Social Change, 150. https://doi.org/10.1016/j.techfore.2019.119792spa
dc.relation.referencesGutiérrez, H., & Salazar, R. (2012). Análisis y Diseño de Experimentos. In SERBIULA (sistema Librum 2.0).spa
dc.relation.referencesGuyader, H., Olsson, L. E., & Friman, M. (2023). Sharing economy platforms as mainstream: balancing pro-social and economic tensions. Total Quality Management and Business Excellence, 34(9–10), 1257–1276. https://doi.org/10.1080/14783363.2022.2159366spa
dc.relation.referencesHagenauer, J., & Helbich, M. (2017). A comparative study of machine learning classifiers for modeling travel mode choice. Expert Systems with Applications, 78, 273–282. https://doi.org/https://doi.org/10.1016/j.eswa.2017.01.057spa
dc.relation.referencesHao, X., Jiang, R., Deng, J., & Song, X. (2022). The Impact of COVID-19 on Human Mobility: A Case Study on New York. 2022 IEEE International Conference on Big Data (Big Data), 4365–4374. https://doi.org/10.1109/BigData55660.2022.10020695spa
dc.relation.referencesHastie, T., Tibshirani, R., & Friedman, J. (2009b). The Elements of Statistical Learning (2nd ed.). Springer New York. https://doi.org/10.1007/978-0-387-84858-7spa
dc.relation.referencesHeaton, J. (2018). Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning. Genetic Programming and Evolvable Machines, 19(1), 305–307. https://doi.org/10.1007/s10710-017-9314-zspa
dc.relation.referencesHensher, D. A. (1994). Stated preference analysis of travel choices: the state of practice. Transportation, 21(2), 107–133. https://doi.org/10.1007/BF01098788spa
dc.relation.referencesHorowitz, J. L. (1991). Reconsidering the multinomial probit model. Transportation Research Part B: Methodological, 25(6), 433–438. https://doi.org/10.1016/0191-2615(91)90036-Ispa
dc.relation.referencesHusson, F., Le, S., & Pagès, J. (2017). Exploratory Multivariate Analysis by Example Using R. In International Statistical Review (2nd ed., Vol. 79, Issue 3). John Wiley & Sons, Ltd. https://doi.org/https://doi.org/10.1111/j.1751-5823.2011.00159_19.xspa
dc.relation.referencesIBM. (2016). Pruebas de la razón de verosimilitud. https://www.ibm.com/docs/es/spss-statistics/saas?topic=model-likelihood-ratio-tests&utm_source=chatgpt.comspa
dc.relation.referencesJamal, A., Zahid, M., Tauhidur Rahman, M., Al-Ahmadi, H. M., Almoshaogeh, M., Farooq, D., & Ahmad, M. (2021). Injury severity prediction of traffic crashes with ensemble machine learning techniques: a comparative study. International Journal of Injury Control and Safety Promotion, 28(4), 408–427. https://doi.org/10.1080/17457300.2021.1928233spa
dc.relation.referencesJohnson, R., & Orme, B. (1996). Getting the Most from CBC. www.sawtoothsoftware.comspa
dc.relation.referencesJolliffe, I. (2002). Principal Component Analysis. https://doi.org/https://doi.org/10.1007/b98835spa
dc.relation.referencesJulagasigorn, P., Banomyong, R., Grant, D. B., & Varadejsatitwong, P. (2021). What encourages people to carpool? A conceptual framework of carpooling psychological factors and research propositions. Transportation Research Interdisciplinary Perspectives, 12. https://doi.org/10.1016/j.trip.2021.100493spa
dc.relation.referencesKalatian, A., & Farooq, B. (2021). Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning. Transportation Research Part C: Emerging Technologies, 124, 102962. https://doi.org/10.1016/J.TRC.2020.102962spa
dc.relation.referencesKanafani, A. K. (1983). Transportation_Demand_Analysis (McGraw-Hill, 1983). Universidad de Michigan.spa
dc.relation.referencesKarlsson, I. C. M., Mukhtar-Landgren, D., Smith, G., Koglin, T., Kronsell, A., Lund, E., Sarasini, S., & Sochor, J. (2020). Development and implementation of Mobility-as-a-Service – A qualitative study of barriers and enabling factors. Transportation Research Part A: Policy and Practice, 131, 283–295. https://doi.org/10.1016/j.tra.2019.09.028spa
dc.relation.referencesKe, J., Yang, H., Zheng, H., Chen, X., Jia, Y., Gong, P., & Ye, J. (2019). Hexagon-Based Convolutional Neural Network for Supply-Demand Forecasting of Ride-Sourcing Services. IEEE Transactions on Intelligent Transportation Systems, 20(11), 4160–4173. https://doi.org/10.1109/TITS.2018.2882861spa
dc.relation.referencesKutner, M. H. ., Nachtsheim, Chris., Neter, John., & Li, William. (2005). Applied linear statistical models. McGraw-Hill Irwin.spa
dc.relation.referencesLawless, J. F. (2002). Statistical Models and Methods for Lifetime Data. https://doi.org/10.1002/9781118033005spa
dc.relation.referencesLeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539spa
dc.relation.referencesLee, A., & Savelsbergh, M. (2015). Dynamic ridesharing: Is there a role for dedicated drivers? Transportation Research Part B: Methodological, 81, 483–497. https://doi.org/10.1016/j.trb.2015.02.013spa
dc.relation.referencesLee, B., Coogan, M., Aultman-Hall, L., & Adler, T. (2016). Rideshare mode potential in non-metropolitan areas of the northeastern United States. Journal of Transport and Land Use, 9(3), 111–126. https://doi.org/10.5198/jtlu.2015.669spa
dc.relation.referencesLevin, I. P. (1982). MEASURING TRADEOFFS IN CARPOOL DRIVING ARRANGEMENT PREFERENCES 71. In Transportation (Vol. 11).spa
dc.relation.referencesLi, J., Embry, P., Mattingly, S. P., Sadabadi, K. F., Rasmidatta, I., & Burris, M. W. (2007). Who chooses to carpool and why? Examination of Texas carpoolers. Transportation Research Record, 2021, 110–117. https://doi.org/10.3141/2021-13spa
dc.relation.referencesLiaw, A., & Wiener, M. (2002). Classification and Regression by randomForest (Vol. 2, Issue 3). http://www.stat.berkeley.edu/spa
dc.relation.referencesLoukaitou-Sideris, A., & Ehrenfeucht, R. (2009). Conflict and Negotiation over Public Space. The MIT Press. http://www.jstor.org/stable/j.ctt5hhh27spa
dc.relation.referencesLundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. Neural Information Processing Systems. https://api.semanticscholar.org/CorpusID:21889700spa
dc.relation.referencesMa, Y., & Zhang, Z. (2020a). Travel Mode Choice Prediction Using Deep Neural Networks with Entity Embeddings. IEEE Access, 8, 64959–64970. https://doi.org/10.1109/ACCESS.2020.2985542spa
dc.relation.referencesMacharis, C., De Witte, A., & Turcksin, L. (2010). The Multi-Actor Multi-Criteria Analysis (MAMCA) application in the Flemish long-term decision making process on mobility and logistics. Transport Policy, 17(5), 303–311. https://doi.org/10.1016/J.TRANPOL.2010.02.004spa
dc.relation.referencesMaddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics. In Econometric Society Monographs. Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9780511810176spa
dc.relation.referencesMarquardt, D. W. (1970). Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation. Technometrics, 12(3), 591–612. https://doi.org/10.1080/00401706.1970.10488699spa
dc.relation.referencesMcFadden, D. (1972). Conditional logit analysis of qualitative choice behavior. https://api.semanticscholar.org/CorpusID:153296073spa
dc.relation.referencesMcFadden, D. (1974). The measurement of urban travel demand. Journal of Public Economics, 3(4), 303–328. https://doi.org/10.1016/0047-2727(74)90003-6spa
dc.relation.referencesMcFadden, D. L. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics. Academic Press. https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdfspa
dc.relation.referencesmineducación. (2023, December). Información Poblacional - SNIES. https://hecaa.mineducacion.gov.co/consultaspublicas/content/poblacional/index.jsfspa
dc.relation.referencesMinitab. (2024). Pruebas de bondad de ajuste para Ajustar modelo logístico binarioy Regresión logística binaria. https://support.minitab.com/es-mx/minitab/help-and-how-to/statistical-modeling/regression/how-to/fit-binary-logistic-model/interpret-the-results/all-statistics-and-graphs/goodness-of-fit-tests/?utm_source=chatgpt.comspa
dc.relation.referencesMitropoulos, L., & Kortsari, A. (2020). RECOMMENDATIONS AND CRITERIA FOR A SUCCESSFUL RIDE-SHARING IN THE IP4 ECOSYSTEM. Ride 2 Rail.spa
dc.relation.referencesMitropoulos, L., Kortsari, A., & Ayfantopoulou, G. (2021). A systematic literature review of ride-sharing platforms, user factors and barriers. In European Transport Research Review (Vol. 13, Issue 1). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s12544-021-00522-1spa
dc.relation.referencesMonchambert, G. (2020). Why do (or don’t) people carpool for long distance trips? A discrete choice experiment in France. Transportation Research Part A: Policy and Practice, 132, 911–931. https://doi.org/10.1016/j.tra.2019.12.033spa
dc.relation.referencesMontgomery, D. C. (2022). Design and Analysis of Experiments (8th ed.).spa
dc.relation.referencesMyers, R. H., Montgomery, D. C., & Anderson-Cook, C. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments. 705.spa
dc.relation.referencesNaciones Unidas. (2024). Las ciudades seguirán creciendo, sobre todo en los países en desarrollo. https://www.un.org/es/desa/2018-world-urbanization-prospectsspa
dc.relation.referencesNagelkerke, N. J. D. (1991). A Note on a General Definition of the Coefficient of Determination. Biometrika, 78(3), 691. https://doi.org/10.2307/2337038spa
dc.relation.referencesNational Household Travel Survey. (2017). Summary of Travel Trends. https://nhts.ornl.gov/.spa
dc.relation.referencesNeoh, J. G., Chipulu, M., & Marshall, A. (2017). What encourages people to carpool? An evaluation of factors with meta-analysis. Transportation, 44(2), 423–447. https://doi.org/10.1007/s11116-015-9661-7spa
dc.relation.referencesNourinejad, M., & Roorda, M. J. (2016). Agent based model for dynamic ridesharing. Transportation Research Part C: Emerging Technologies, 64, 117–132. https://doi.org/10.1016/j.trc.2015.07.016spa
dc.relation.referencesNúñez, E., Steyerberg, E. W., & Núñez, J. (2011). Estrategias para la elaboración de modelos estadísticos de regresión. Revista Espanola de Cardiologia, 64(6), 501–507. https://doi.org/10.1016/j.recesp.2011.01.019spa
dc.relation.referencesObservatorio de Movilidad. (2023, December). Resultados Encuesta de Movilidad. https://www.encuestademovilidad2023.com/spa
dc.relation.referencesOlsson, L. E., Maier, R., & Friman, M. (2019). Why do they ride with others? Meta-analysis of factors influencing travelers to carpool. In Sustainability (Switzerland) (Vol. 11, Issue 8). MDPI. https://doi.org/10.3390/su11082414spa
dc.relation.referencesOrozco-Fontalvo, M., & Moura, F. (2023). Refocusing MaaS approach: A brief. Transport Policy, 141, 340–342. https://doi.org/10.1016/J.TRANPOL.2023.08.002spa
dc.relation.referencesOrozco-Fontalvo, M., Soto, J., Arévalo, A., & Oviedo-Trespalacios, O. (2019). Women’s perceived risk of sexual harassment in a Bus Rapid Transit (BRT) system: The case of Barranquilla, Colombia. Journal of Transport & Health, 14, 100598. https://doi.org/10.1016/J.JTH.2019.100598spa
dc.relation.referencesOrtuzar, J., & Willumsen, L. (1994). Modeling Trasport (John Wiley & Son., Ed.).spa
dc.relation.referencesPineda-Jaramillo, J., & Arbelaez, O. (2022). Assessing the Performance of Gradient-Boosting Models for Predicting the Travel Mode Choice Using Household Survey Data. Journal of Urban Planning and Development, 148. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000830spa
dc.relation.referencesQin, X. (2024). Sample size and power calculations for causal mediation analysis: A Tutorial and Shiny App. Behavior Research Methods, 56(3), 1738–1769. https://doi.org/10.3758/s13428-023-02118-0spa
dc.relation.referencesQuinones, L. M. (2020). Sexual harassment in public transport in Bogotá. Transportation Research Part A: Policy and Practice, 139, 54–69. https://doi.org/10.1016/J.TRA.2020.06.018spa
dc.relation.referencesRao, C. (1948). Large sample tests of statistical hypotheses concerning several parameters with applications to problems of estimation. Mathematical Proceedings of the Cambridge Philosophical Society, 44(1), 50–57. https://doi.org/DOI: 10.1017/S0305004100023987spa
dc.relation.referencesRibeiro, M., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. https://doi.org/10.18653/v1/N16-3020spa
dc.relation.referencesRodriguez-Valencia, A., Rosas-Satizábal, D., & Hidalgo, D. (2023). Big effort, little gain for users: lessons from the public transport system reform in Bogotá. Public Transport, 15, 411–433. https://doi.org/10.1007/s12469-022-00308-1spa
dc.relation.referencesRohács, J., & Rohács, D. (2020). Total impact evaluation of transportation systems. Transport, 35(2), 193–202. https://doi.org/10.3846/transport.2020.12640spa
dc.relation.referencesSalmerón, R., García Pérez, J., López Martín, M. D. M., & García, C. G. (2016). Collinearity diagnostic applied in ridge estimation through the variance inflation factor. Journal of Applied Statistics, 43(10), 1831–1849. https://doi.org/10.1080/02664763.2015.1120712spa
dc.relation.referencesSecretaría Distrital de Movilidad. (2022). ESTUDIO TÉCNICO DE EVALUACIÓN E IMPLEMENTACIÓN DE AJUSTES EN MEDIDAS DE MOVILIDAD PARA VEHÍCULOS PARTICULARES EN LA CIUDAD DE BOGOTÁ. www.movilidadbogota.gov.cospa
dc.relation.referencesSeguel, R. J., Gallardo, L., Osses, M., Rojas, N. Y., Nogueira, T., Menares, C., De Fatima Andrade, M., Belalcázar, L. C., Carrasco, P., Eskes, H., Fleming, Z. L., Huneeus, N., Ibarra-Espinosa, S., Landulfo, E., Leiva, M., Mangones, S. C., Morais, F. G., Moreira, G. A., Pantoja, N., … Yoshida, A. C. (2022). Photochemical sensitivity to emissions and local meteorology in Bogotá, Santiago, and São Paulo: An analysis of the initial COVID-19 lockdowns. Elementa, 10(1). https://doi.org/10.1525/ELEMENTA.2021.00044/169476spa
dc.relation.referencesShaheen, S. A., Chan, N. D., & Gaynor, T. (2016). Casual carpooling in the San Francisco Bay Area: Understanding user characteristics, behaviors, and motivations. Transport Policy, 51, 165–173. https://doi.org/10.1016/j.tranpol.2016.01.003spa
dc.relation.referencesShaheen, S., & Cohen, A. (2019). Shared ride services in North America: definitions, impacts, and the future of pooling. In Transport Reviews (Vol. 39, Issue 4, pp. 427–442). Routledge. https://doi.org/10.1080/01441647.2018.1497728spa
dc.relation.referencesShaheen, S., Cohen, A., & Bayen, A. (2018). The Benefits of Carpooling. The Environmental and Economic Value of Sharing a Ride. UC Berkeley Recent Work. https://doi.org/10.7922/G2DZ06GFspa
dc.relation.referencesShahram, T., Lina, K., & Brian, B. (2015). Propensity to participate in a peer-to-peer social-network-based carpooling System. Journal of Advanced Transportation , 50.spa
dc.relation.referencesSimancas, W., Vinasco, C., Rosas-Satizábal, D., Alberto Ortiz-Ramirez, H., & Rodriguez-Valencia, A. (2024a). Decision-making in open carpooling programs: Perspectives of drivers versus passengers. Travel Behaviour and Society, 36, 100759. https://doi.org/10.1016/J.TBS.2024.100759spa
dc.relation.referencesSimon, H. (2010). Neural_Networks_and_Learning_Machines (Prentice Hall, Ed.; 3rd ed.).spa
dc.relation.referencesSims, R., Schaeffer, R., Creutzig, F., Cruz-Núñez, X., D’Agosto, M., Dimitriu, D., Meza, M. J., Fulton, L., Kobayashi, S., Lah, O., Mckinnon, A., Newman, P., Ouyang, M., Schauer, J. J., Sperling, D., & Tiwari, G. (2014). Transport. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/CBO9781107415416.005spa
dc.relation.referencesSkouby, K. E., Kivimäki, A., Lotta, H., Per, L., & Iwona, W. (2014). Smart Cities and the Ageing Population.spa
dc.relation.referencesStark, J., & Meschik, M. (2018). Women’s everyday mobility: Frightening situations and their impacts on travel behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 54, 311–323. https://doi.org/10.1016/J.TRF.2018.02.017spa
dc.relation.referencesTalele, T., Gauresh, P., & Parimal, D. (2012). Dynamic Ridesharing Using Social Media. International Journal on AdHoc Networking Systems, 2(4), 29–36. https://doi.org/10.5121/ijans.2012.2403spa
dc.relation.referencesTomTom Traffic Index. (2023). Ranking 2023. https://www.tomtom.com/traffic-index/ranking/spa
dc.relation.referencesToro-González, D., Cantillo, V., & Cantillo-García, V. (2020). Factors influencing demand for public transport in Colombia. Research in Transportation Business & Management, 36, 100514. https://doi.org/10.1016/J.RTBM.2020.100514spa
dc.relation.referencesTrain, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9780511805271spa
dc.relation.referencesUllah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2021). Electric vehicle energy consumption prediction using stacked generalization: an ensemble learning approach. International Journal of Green Energy, 18(9), 896–909. https://doi.org/10.1080/15435075.2021.1881902spa
dc.relation.referencesValliant, R. , Dever, J. A. , & Kreuter, F. (2018, October 13). Power and Sample Size Determination. https://doi.org/doi.org/10.1007/978-3-319-93632-1_4spa
dc.relation.referencesvan Cranenburgh, S., Wang, S., Vij, A., Pereira, F., & Walker, J. (2022). Choice modelling in the age of machine learning - Discussion paper. Journal of Choice Modelling, 42, 100340. https://doi.org/10.1016/J.JOCM.2021.100340spa
dc.relation.referencesVanoutrive, T., Van De Vijver, E., Van Malderen, L., Jourquin, B., Thomas, I., Verhetsel, A., & Witlox, F. (2012). What determines carpooling to workplaces in Belgium: Location, organisation, or promotion? Journal of Transport Geography, 22, 77–86. https://doi.org/10.1016/j.jtrangeo.2011.11.006spa
dc.relation.referencesVuchkov, I., & Boyadjieva, L. (2001). Quality Improvement with Design of Experiments A Response Surface Approach.spa
dc.relation.referencesWald, A. (1943). Tests of statistical hypotheses concerning several parameters when the number of observations is large. Transactions of the American Mathematical Society, 54, 426–482. https://api.semanticscholar.org/CorpusID:54174575spa
dc.relation.referencesWang, R. (2011). Shaping carpool policies under rapid motorization: The case of Chinese cities. Transport Policy, 18(4), 631–635. https://doi.org/10.1016/j.tranpol.2011.03.005spa
dc.relation.referencesWang, Y., Gu, J., Wang, S., & Wang, J. (2019). Understanding consumers’ willingness to use ride-sharing services: The roles of perceived value and perceived risk. Transportation Research Part C: Emerging Technologies, 105, 504–519. https://doi.org/10.1016/j.trc.2019.05.044spa
dc.relation.referencesWang, Y., Winter, S., & Ronald, N. (2017). How much is trust: The cost and benefit of ridesharing with friends. Computers, Environment and Urban Systems, 65, 103–112. https://doi.org/10.1016/j.compenvurbsys.2017.06.002spa
dc.relation.referencesWang, Y., Winter, S., & Tomko, M. (2018). Collaborative activity-based ridesharing. Journal of Transport Geography, 72, 131–138. https://doi.org/10.1016/j.jtrangeo.2018.08.013spa
dc.relation.referencesWang, Z., Chen, X., & Chen, X. (Michael). (2019). Ridesplitting is shaping young people’s travel behavior: Evidence from comparative survey via ride-sourcing platform. Transportation Research Part D: Transport and Environment, 75, 57–71. https://doi.org/10.1016/j.trd.2019.08.017spa
dc.relation.referencesWebb, R., Bai, X., Smith, M. S., Costanza, R., Griggs, D., Moglia, M., Neuman, M., Newman, P., Newton, P., Norman, B., Ryan, C., Schandl, H., Steffen, W., Tapper, N., & Thomson, G. (2017). Sustainable urban systems: Co-design and framing for transformation. https://doi.org/10.1007/s13280-017-0934-6spa
dc.relation.referencesWilkowska, W., Farrokhikhiavi, R., Ziefle, M., & Vallée, D. (2018). Mobility requirements for the use of carpooling among different user groups. Advances in Human Factors, Software, and Systems Engineering, 4. https://doi.org/10.54941/ahfe100134spa
dc.relation.referencesWillis, K. G. (2014). The Use of Stated Preference Methods to Value Cultural Heritage. Handbook of the Economics of Art and Culture, 2, 145–181. https://doi.org/10.1016/B978-0-444-53776-8.00007-6spa
dc.relation.referencesYee, T. W. (2015). Vector Generalized Linear and Additive Models Springer Series in Statistics. http://www.springer.com/series/692spa
dc.relation.referencesYu, B., Ma, Y., Xue, M., Tang, B., Wang, B., Yan, J., & Wei, Y. M. (2017). Environmental benefits from ridesharing: A case of Beijing. Applied Energy, 191, 141–152. https://doi.org/10.1016/j.apenergy.2017.01.052spa
dc.relation.referencesZahid, M., Chen, Y., & Jamal, A. (2020). Short term traffic state prediction via hyperparameter optimization based classifiers. Mdpi.ComM Zahid, Y Chen, A Jamal, MQ MemonSensors, 2020•mdpi.Com. https://doi.org/10.3390/s20030685spa
dc.relation.referencesZhang, Y., & Haghani, A. (2015). A gradient boosting method to improve travel time prediction. Transportation Research Part C: Emerging Technologies, 58, 308–324. https://doi.org/10.1016/J.TRC.2015.02.019spa
dc.relation.referencesZhao, X., Yan, X., Yu, A., & Van Hentenryck, P. (2020). Prediction and Behavioral Analysis of Travel Mode Choice: A Comparison of Machine Learning and Logit Models. Travel Behaviour and Society, 20, 22–35. https://doi.org/10.1016/j.tbs.2020.02.003spa
dc.relation.referencesZubaryeva, A., Thiel, C., Zaccarelli, N., Barbone, E., & Mercier, A. (2012). Spatial multi-criteria assessment of potential lead markets for electrified vehicles in Europe. Transportation Research Part A: Policy and Practice, 46(9), 1477–1489. https://doi.org/10.1016/j.tra.2012.05.018spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc380 - Comercio , comunicaciones, transporte::388 - Transportespa
dc.subject.proposalMovilidad compartidaspa
dc.subject.proposalServicios de transporte informalspa
dc.subject.proposalEconomía colaborativaspa
dc.subject.proposalTransporte universitariospa
dc.subject.proposalAnálisis de correspondencia múltiplespa
dc.subject.proposalModelos Logitspa
dc.subject.proposalAprendizaje automáticospa
dc.subject.proposalShared mobilityeng
dc.subject.proposalInformal transport serviceseng
dc.subject.proposalCollaborative economyeng
dc.subject.proposalUniversity transporteng
dc.subject.proposalMultiple correspondence analysiseng
dc.subject.proposalLogit modelseng
dc.subject.proposalMachine learningeng
dc.subject.unescoAnálisis estadísticospa
dc.subject.unescoStatistical analysiseng
dc.subject.unescoActitud del estudiantespa
dc.subject.unescoStudent attitudeseng
dc.subject.unescoTransporte urbanospa
dc.subject.unescoUrban transporteng
dc.titleAnálisis de la demanda actual y potencial, factores motivadores y barreras, para la implementación de servicios de carpooling en comunidades universitarias de Bogotáspa
dc.title.translatedAnalysis of current and potential demand, motivating factors, and barriers to implementing carpooling services in Bogotá's university communitieseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentResponsables políticosspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
oaire.awardtitleIdentificación de factores desencadenantes, motivadores y barreras para la adopción de servicios de movilidad compartida en la transición hacia un MaaS - Convocatoria para el Apoyo a Proyectos de Investigación, Creación Artística e Innovación de la Sede Bogotá de la Universidad Nacional de Colombia 2022-2024
oaire.fundernameUniversidad Nacional de Colombiaspa

Archivos

Bloque original

Mostrando 1 - 2 de 2
Cargando...
Miniatura
Nombre:
1019033558.2025.pdf
Tamaño:
2.38 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería - Transporte
Cargando...
Miniatura
Nombre:
1019033558.2025.zip
Tamaño:
10.86 MB
Formato:
Comprimido
Descripción:
Anexo 1

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: