DOI:
https://doi.org/10.14483/2248762X.12495Publicado:
2017-09-08Número:
Edición especial, Enero - Junio de 2017Sección:
InvestigaciónAnálisis de las fatalidades por accidentes de tránsito en Colombia acontecidos en el periodo 2011-2015
Palabras clave:
Accidentalidad, Seguridad Vial, Tránsito (es).Descargas
Resumen (es)
Haciendo un análisis estadístico, este artículo muestra cómo se han aumentado las fatalidades por accidentes de tránsito en Colombia, reflejando un incremento año tras año. Se puede verificar que la población más vulnerable se encuentra en el rango de los 21 a los 30 años, tanto de hombres como de mujeres. Es importante analizar estas estadísticas para buscar una solución y lograr una disminución de fatalidades que actualmente tienen un costo muy alto para la sociedad y la economía colombiana. En los resultados analizados se ha evidenciado el aumento de la accidentalidad y fallecimiento de personas, lo que conduce a concluir que posiblemente no han sido efectivas las políticas gubernamentales para tratar de mitigar los elevados índices en cuestión.Referencias
AASHTO. 2004. A Policy on Geometric Design of Highways and Streets. American Association of State Highway and Transportation Officials. Washington, D.C., USA.
Af Wåhlberg, A.E., 2012. Changes in driver celeration behaviour over time: Do drivers learn from collisions? Transportation Research Part F: Traffic Psychology and Behaviour, 15(5), pp.471–479.
Alicea, L. 2004. Analysis and Evaluation of Crashes Involving Pedestrians in Puerto Rico. Tesis de Maestría en Ingeniería Civil, Recinto Universitario de Mayagüez, Universidad de Puerto Rico.
Alonso, M., 2016. La integración del factor
humano en el ámbito técnico de la gestión de las carreteras y la seguridad vial: Un enfoque investigativo. Available at: http://roderic.uv.es/handle/10550/51943.
Arias, W., Colucci, B., 2006. Road Safety Audit. , 19(3), p.28.
Arasan, V. & Dhivya, G. (2008). Measuring Heterogeneous Traffic Density. Proceedings of international conference on sustainable urbn transport and enviroment (pp. 342-346) Bangok: World Academy of Science.
Arasan, V. & Dhivya, G. (2010). Simulation of Highly heterogeneous traffic flow characteristics. Proceedings of the 24th european conference on modelling and simulation (pp. 81-87). Kualalumpur, Malaysia.
Arasan, V. & Koshy, R.Z. (2005). Methodology for medelling highly heterogeneous traffic flow. Journal of Transportation Engineering, 131(7), 544-551.
Bella, F., 2014. Effects of Combined Curves on Driver’s Speed Behavior: Driving Simulator Study. Transportation Research Procedia, 3, pp.100–108.
Ben-Bassat, T. & Shinar, D., 2011. Effect of shoulder width, guardrail and roadway geometry on driver perception and behavior. Accident Analysis and Prevention, 43(6), pp.2142–2152. Available at: http://www.sciencedirect.com/science/article/pii/S0001457511001709 [Accessed April 5, 2016].
Boyce, T.E. & Geller E.S. (2001). A technology to measure multiple driving behaviors without sefl-report or participant reactivity. Journal of applied Behavior Analisys, 34(1), 39-35
Brackstone, M. & McDonald, M. (1999). Car-following: a historical review. Transportation ResearchPart F: Traffic Psychology and Behavior, 2(4), 181-196.
Camacho, J., Medina, S., Terán, O (2012). Simulación del tráfico de autos en una intersección: desde la perspectiva de una plataforma multiagente. Revista Ciencia e Ingeniería, 33(2), 85-94.
Cherri, C., Nodari, E., Toffetti, A. (2004). AIDE Subproject 2: Review of existing tools and methods (Tech. Inf.), Information Society Technologies Programme " Adaptive integrated driver-vehicle interface" (AIDE)
Chowdhury, D. Wolf, D.E., Schreckenberg, M., (1997), Paticle hopping models for two-lane traffic with two kindas of cehicle: Effects of lane-changing rules. Physica A, 235, 417-439.
Cobos, C. Modelo de un meta buscador que realiza agrupación de documentos web, enriquecido con una taxonomía, ontologías e información del usuario, tesis doctoral Ingeniería de Sistemas, Universidad Nacional de Colombia, 2013.
Daganzo, C.F. (1994), the cell transmission model: A dynanic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4), 269-287.
Daganzo, C.F. (1995), the cell transmission model part II: Network traffic. Transportation Research Part B: Methodological, 29(2), 79-93.
Daganzo, C.F. (1997), Fundamentals of
transportation and traffic operations, (E. Science Ed.) Oxford.
Dans. E. Disponible en:http://www.enriquedans. com/2011/10/big-data-una-pequena- introduccion.html, 2011.
Ellison, A.B., Greaves, S.P. & Bliemer, M.C.J., 2015. Driver behaviour profiles for road safety analysis. Accident Analysis & Prevention, 76, pp.118–132.
Ferrer, A., Smith, R. & Cuellar, M., 2013. Análisis de la Capacidad de Gestión de la Seguridad Vial. Banco Muncial, pp.92–93.
Figueroa, A., 2005. Note To Users. Purdue University.
Fire, M. et al., 2012. Data mining opportunities in geosocial networks for improving road safety. 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2012.
Georgiou, T. et al., 2015. Mining Complaints for Traffic-Jam Estimation. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM ’15, pp.330–335. Available at: http://dl.acm.org/citation.cfm?doid=2808797.2809404.
Gipps, P.G., (1986), A model for the structure of lane-changing decisions, Transportation Research Part B: Methodological, 20(5), 107-120.
Gu, Y., Qian, Z. (Sean) & Chen, F., 2016. From Twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67, pp.321–342. Available at: http://www.sciencedirect.com/science/article/pii/S0968090X16000644.
Gurupackiam, S., Jones, S., Turner, D. (2010), Characterization of arterial traffic congestion nthrough analysis of operational parameters (gap acceptance and lane changing) (Inf. Tech. No. UTCA 07112). University Tranbsportation Center for Alabama [UTCA].
Hamdar, S.H. & Schorr, J., 2013. Interrupted versus uninterrupted flow: a safety propensity index for driver behavior. Accident; analysis and prevention, 55, pp.22–33. Available at: http://www.sciencedirect.com/science/article/pii/S0001457513000420 [Accessed March 11, 2016].
Hassan, H.M. & Abdel-Aty, M.A., 2013. Exploring the safety implications of young drivers’ behavior, attitudes and perceptions. Accident Analysis and Prevention, 50, pp.361–370. Available at: http://www.sciencedirect.com/science/article/pii/S0001457512001558 [Accessed March 11, 2016].
Iversen, H.H.& Rundmo, T., 2012. Changes in Norwegian drivers’ attitudes towards traffic safety and driver behaviour from 2000 to 2008. Transportation Research Part F: Traffic Psychology and Behaviour, 15(2), pp.95–100.
Jacobson, D. Brail G. Woods, D., 2012. APIs: A Strategy Guide First Rele, Sebastopol, CA. O’Reilly Media Inc. Available at: www.oreilly.com.
Jiang, S. et al., 2016. The TimeGeo modeling framework for urban motility without travel surveys. Proceedings of the National Academy of Sciences, (August),
p.201524261. Available at: http://www.pnas.org/lookup/doi/10.1073/pnas.1524261113.
Kesting, A., Trieber, M. Helbing,. D. (2009). Agents for traffic Simulation. in Uhrmacher A. & Weyns, D. (Ed.), Multi-agent systems. Simulation and applications (pp. 325-356), Crc Press. Available at http://arxiv.org/abs/0805.0300.
Khoo, H.L. & Asitha, K.S., 2016. Quantifying impact of traffic images applications (APPS) on travel choices. KSCE Journal of Civil Engineering, 20(2), pp.899–912.
Lang, L. W, & Chang, C.-W., (2003), Motorbike´s moving behavior in mixed traffic: Particle hopping model with cellular automata. Journal of the Eastern ASIA Society for Transportation Studies, 5, 23-27.
Lang, L. W, & Chang, C.-W., (2005), Inhomogeneous Cellular Automata Modeling for Mixed Traffic with Cars and Motorcycles. Journal of Advance Transportatiom, 39(3), 323-349.
Lang, L. W, Choiu, Y-C., Lin, Z., S., Hsu, C.-C., (2010) Cellular automaton simulations for mixed traffic with erratic motorcycles behaviours. Physica A: Stitistical Mechanics and its applications, 389 (10), 2077-2089.
Lee, H.C., Cameron, D., Lee, A. H., (2003). Assessing the driving performance of older adult drivers: on-road versus simulated driving. Accident Analysis & Prevention, 35(5), 797-803.
Lee, T.-C., (2007), An Agent based Model of Simulated Motorcycle Behaviour in Mixed Traffic Flow. Tesis Dcotoral no publicada. Imperial College. London UK.
Lee, X.-G., GAO, Z.-Y., Jiang, R. (2006), A realistic two-lane cellular automata traffic model considering aggresive lane-changing behavior of fast vehicle. Physica A, 367, 479-486
Liu, H. (2008), Travel time prediction for urban network: PhD Thesis, Deft University of Technology.
Mallikarjuna, C., Rao, K.R. (2009), Cellular Automata Model for Heterogeneous Traffic, Journal of Advance Transportation, 43 (3), 321-345.
University of Technology.
Mallikarjuna, C., Rao, K.R. (2011), Heterogeneous traffic flow modelling: a complete methodology, Transportmetrica, 7(5), 321-345
Mu, R., Ya,amoto, T., (2013) An Analysis on Mixed Traffci Flow of Conventional Passenger cars and Microicars Using a Cellular Automata Model. Procedia - Social and Behavioral Sciences . 43(0), 457-465
Musicant, O., Lotan, T. & Albert, G., 2015. Do we really need to use our smartphones while driving? Accident; analysis and prevention, 85, pp.13–21. Available at: http://www.sciencedirect.com/science/article/pii/S0001457515300555 [Accessed April 16, 2016].
Nagel, K., Schreckenberg, M (1992), A cellular automaton model for freeway traffic. Journal of Physique I, 2(12), 2221-2229.
Norza, E. Useche, S. Moreno, J. Granados, E. Romero, M., 2014. Componentes
descriptivos y explicativos de la accidentalidad vial en Colombia: incidencia del factor humano. Revista Criminalidad, 56(1), pp.157–187.
Ram, T. & Chand, K., 2016. Effect of drivers’ risk perception and perception of driving tasks on road safety attitude. Transportation Research Part F: Traffic Psychology and Behaviour.
Roman, G.D. et al., 2015. Novice drivers’ individual trajectories of driver behavior over the first three years of driving. Accident Analysis & Prevention, 82, pp.61–69.
Salgado, M. Oracle apuesta por Big Data con tecnología y proyectos. Disponible en: http://www.computerworld.es/big-data/ oracle-apuesta-por-big-data-con-tecnologia-y- proyectos, 2014
Scialfa, C.T. et al., 2011. A hazard perception test for novice drivers. Accident Analysis & Prevention, 43(1), pp.204–208.
Smith, P.M. et al., 2015. The development of a conceptual model and self-reported measure of occupational health and safety vulnerability. Accident; analysis and prevention, 82, pp.234–43. Available at: http://www.sciencedirect.com/science/article/pii/S0001457515002286 [Accessed September 19, 2015].
Tang, T.-O., Huang, H.-J., Shang, H.-Y., (2010), A dymanic model for the heterogeneous traffic flow consisting of car, bycicle and pedestrian. International Journal of Modern Physics C, 21(02), 159-176.
Vasic, J., Rukin, H.J., (2012), A CA-Based Model for City Traffic Including Bicycles. in S. Polyzos (Ed.) Urban development (pp. 79-92) In Tech. Available at http//intechopen.com/books/urban-development/a-ca-based-model-for-city-traffic-including-bicycles.
Vasic, J., Rukin, H.J., (2012) Cellular automata simulation of traffic including cars and bicycles, Physica A, 391(8), 2720-2729.
Viola, P. Jones, M. (2001) Rapid object detection using a boosted cascade of simple features. In Proceedings of the 20014 ieee computer society conference on computer vision and pattern recognition, (Vol 1, pp. I-511)
Warner, H.W. & Åberg, L., 2014. Drivers’ tendency to commit different aberrant driving behaviours in comparison with their perception of how often other drivers commit the same behaviours. Transportation Research Part F: Traffic Psychology and Behaviour, 27, pp.37–43.
Wu, X., Zhu, X., Wu, G.-Q., Ding, W., Data mining with big data, IEEE transactions on knowledge and data engineering, Vol. 26, No 1, 2014.
Zamith, M. et al., 2015. A new stochastic cellular automata model for traffic flow simulation with drivers’ behavior prediction. Journal of Computational Science, 9, pp.51–56.
Zheng, F. (2011) Modelling Urban Travel Times. PhD Thesis, Defl University of Technology.
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
Licencia
Reconocimiento – NoComercial – CompartirIgual (by-nc-sa): No se permite el uso comercial de la obra original, las obras derivadas deben circular con las mismas condiciones de esta licencia realizando la correcta atribución al autor.
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional