Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)Baena Vasquez, Alejandra JulietteMartínez Huartos, Johann HeinzSalcedo Fontecha, Juan Paulo2021-03-082021-03-082020-06-11http://repositorio.uan.edu.co/handle/123456789/2795Mobility and road safety studies are essential in the mitigation of the effects generated by traffic accidents worldwide. Therefore, given the complexity in these aspects of the Bogotá city, a study of road accidents is carried out using Network Science in 7 years range and at a spatial resolution by Traffic Analysis Zones (TAZ) that divide to the city into 922 polygons according to the latest mobility survey 2019. The results show some peculiarities in its dynamics, identify the areas that have synchronous behaviors, anomalous road corridors with low vehicle flow and high accident rates and quantify the spacetime entropy of traffic accidents.Los estudios de movilidad y seguridad vial, son fundamentales en la mitigación de los efectos generados por los accidentes de tránsito a nivel mundial. Por lo tanto, dada la complejidad en estos aspectos de la ciudad de Bogotá, se realiza un estudio de la accidentalidad vial utilizando Ciencia de Redes en una ventana temporal de 7 años y a una resolución espacial por Zonas de Análisis de Transporte (ZAT) que dividen a la ciudad en 922 polígonos de acuerdo a la última encuesta de movilidad 2019. Los resultados permiten evidenciar algunas particularidades en su dinámica, identificar las zonas que tienen comportamientos sincrónicos, corredores viales anómalos con bajo flujo vehicular y alta accidentalidad y cuantificar la entropía espacio-temporal de los accidentes de tránsitospaAcceso abiertoMovilidadCiencia de redesComplejidadEntropíaZATTesis y disertaciones (Maestría y/o Doctorado)MobilityNetwork ScienceComplexityEntropyTAZinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Abdulhafedh, A. (2016). Crash frequency analysis. Journal of Transportation Technologies, 06(04):169-180.Abdulhafedh, A. (2017). Road crash prediction models: different statistical modeling approaches. Journal of Transportation Technologies, 07(02):190-205.Agencia Nacional de Seguridad Vial (2020). Observatorio de la seguridad vial. https://ansv.gov.co/observatorio/index.html. [Online; último acceso 9-abril-2020].Amézquita, L., Durán, D., and Fajardo, D. (2016). Matriz origen-destino y efi ciencia en modos de transporte urbano: Un análisis de la movilidad de Bogotá. Semestre Económico, 19(39):91-112.Barbosa, H., Barthelemy, M., Ghoshal, G., R.J., C., Lenormand, M., Louail, T., Menezes, R., Ramasco, J., and Simini, F., T. M. (2018). Human mobility: Models and applications. Physics Reports, 734:1-74.Buchanan, M. (2008). Laws, power laws and statistics. Nature Physics, 40:339.Clauset, A., Shalizi, C. R., and Newman, M. E. J. (2009). Power-law distributions in empirical data. SIAM review, 51(4):661{703.Congreso de la República de Colombia (2016). Ley 1503 de 2011. http://www.secretariasenado.gov.co/senado/basedoc/ley_1503_2011.html. [Online; accessed 9-abril-2020].Driss, M., Benabdeli, K., Saint-Gerand, T., and Hamadouche, M. (2015). Traffic safety prediction model for identifying spatial degrees of exposure to the risk of road accidents based on fuzzy logic approach. Geocarto International, 30(3).El-adaway, I. (2012). Analyzing traffic layout using dynamic social network analysis. http://www.ncitec.msstate.edu/wp-content/uploads/2012-07FR.pdf. [Online; último acceso 5-mayo-2020].Estrada, E. (2016). The structure of complex networks: Theory and applications. Oxford University Press.Estrada, E. and Knight, P. (2015). A first course in network theory. Oxford University Press.Girvan, M. and Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12):7821-7826.Heinrich, H. W. (1969). Industrial accident prevention: a scientific approach. McGraw-Hill.Klockner, K. and Toft, Y. (2015). Accident modelling of railway safety occurrences: The safety and failure event network (safe-net) method. Procedia Manufacturing, 3:1734-1741.Lin, L., Wang, Q., and Sadek, A. W. (2014). Data mining and complex network algorithms for traffic accident analysis. Transportation Research Record: Journal of the Transportation Research Board, 2460(1):128-136.Lord, D. (2006). Modeling motor vehicle crashes using poisson-gamma models: Examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter. Accident Analysis and Prevention, 38(4):751-766.Lotero, L., Hurtado, R. G., Floría, L. M., and Gómez, J. (2016). Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes. R Soc Open Sci, 3:1-12.Martín, L., Baena, L., Garach, L., López, G., and Oña, J. (2014). Using data mining techniques to road safety improvement in spanish roads. Procedia - Social and Behavioral Sciences, 160:607-614.Martínez, J. H. (2015). Analysing brain dynamics by means of networks science. PhD thesis, Universidad Politécnica de Madrid, Spain. Tesis de Doctorado.Martínez, J. H., López, M. E., Ariza, P., Chavez, M., Pineda, J. A., López, D., Gil, P., Maestú, F., and Buldú, J. M. (2018). Functional brain networks reveal the existence of cognitive reserve and the interplay between network topology and dynamics. Scientific Reports, 8(1):1-11.Martínez, L., Viegas, J., and Silva, E. (2009). A traffic analysis zone de nition: A new methodology and algorithm. Transportation, 36:581-599.Ministerio de Transporte (2020). Código nacional de tránsito. https://www.mintransporte.gov.co/documentos/17/leyes/. [Online; último acceso 9-abril-2020].Mohammed, A., Ambak, K., Mosa, A., and D., S. (2018). Classification of traffic accident prediction models: A review paper. pages 7-9.Newman, M. E. J. (2013). Networks: An introduction. The Journal of Mathematical Sociology, 7(4):250-251.Norza, E., Romero, M., Moreno, J., Díaz, R., Useche, S., and Gómez, I. (2013). Caracterización de la accidentalidad en Colombia: Análisis del fenómeno desde el estudio del factor humano.Organización Mundial de la Salud (2015). Informe sobre la situación mundial de la seguridad vial 2015. https://www.who.int/violence_injury_prevention/road_safety_status/2015/es/. [Online; último acceso 9-abril-2020].Qureshi, Z. H. (2008). A review of accident modelling approaches for complex critical sociotechnical systems.Ramasco, J., Dorogovtsev, S. N., and Pastor, R. (2004). Self-organization of collaboration networks. Physical Review E, 70(3).Secretaría de Educación del Distrito (2020). Guías para la elaboración de planes de movilidad escolar. https://www.educacionbogota.edu.co. [Online; último acceso 7-mayo-2020].Shealy, J. E. (1979). Impact of theory of accident causation on intervention strategies. Proceedings of the Human Factors Society Annual Meeting, 23(1):225-229.Soriano, D., Lotero, L., Arenas, A., and Gómez, J. (2018). Spreading processes in multiplex metapopulations containing different mobility networks. Physical Review X, 8(3).Stanton, W. A. and Willenbrock, J. H. (1990). Conceptual framework for computerbased, construction safety control. Journal of Construction Engineering and Management, 116(3):383-398.Wang, X., Jin, Y., M., A., Tremont, P. J., and Xiaohong, C. (2012). Macrolevel model development for safety assessment of road network structures. Transportation Research Record: Journal of the Transportation Research Board, 2280(1):100-109.World Health Organization (2010). A decade of action for road and safety: A brief planning document. https://www.who.int/roadsafety/Decade_of_action.pdf. [Online; último acceso 9-abril-2020].World Health Organization (2018). Who in countries-statistics. https://www.who.int/data/gho/data/countries. [Online; último acceso 16-abril-2020].Xiugang, L., Lord, D., Zhang, Y., and Xie, Y. (2008). Predicting motor vehicle crashes using support vector machine models. Accident Analysis and Prevention, 40:1611-8.instname:Universidad Antonio Nariñoreponame:Repositorio Institucional UANrepourl:https://repositorio.uan.edu.co/