Predicción de accidentes viales en Cartagena, Colombia, con árboles de decisión y reglas de asociación

  • Holman Ospina-Mateus Universidad Tecnológica de Bolívar
  • Leonardo Augusto Quintana Jiménez Pontificia Universidad Javeriana
Palabras clave: Colombia, accidentalidad vial, severidad, minería de datos, predicción, reglas

Resumen

El objetivo principal de esta investigación es predecir los factores asociados con la severidad en los accidentes viales de Cartagena (Colombia), la metodología está basada en técnicas de minería de datos como arboles de decisión (J48) y reglas de asociación (soporte, confianza, Lift). La investigación fue desarrollada con 10.053 registros de accidentes de tráfico entre 2016 y 2017, por medio del uso del Software WEKA (Waikato Environment for Knowledge Analysis). En el análisis, la severidad fue definida de bajo riesgo (daños materiales), y alto riesgo (victimas heridas y fatales), y su validación consideró la técnica transversal 10fold. Entre los resultados más significativos, se evidenció que los motociclistas y ciclistas son los actores viales más vulnerables, además los moto–usuarios entre los 20–39 años son propensos a accidentes viales con alta severidad. Finalmente, los factores de accidentalidad vial identificados ayudan a promover contramedidas para mejorar la seguridad vial de la ciudad.

Descargas

La descarga de datos todavía no está disponible.

Citas

Abdelwahab, Hassan., y Abdel-Aty, Mohamed (2001), “Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections”, Transportation Research Record: Journal of the Transportation Research Board Vol. 1746, No. 1.

Agrawal, Rakesh., Imielinski, Tomasz., y Swami, Arun (1993), “Mining association rules between sets of items in large databases”, Acm sigmod record, ACM Vol. 22, No. 2.

Albalate, Daniel y Fernández-Villadangos, Laura (2010), “Motorcycle injury severity in Barcelona: The role of vehicle type and congestion”, Traffic Injury Prevention Vol. 11, No. 6.

Bourdet, Nicolas., Deck, Caroline., Tinard, Violaine., y Rémy. Willinger (2012), “Behaviour of helmets during head impact in real accident cases of motorcyclists”, International Journal of Crashworthiness Vol. 17, No. 1.

Brown, Julie., Schonstein, Lisa., Ivers, Rebecca., y Keay, Lisa (2018), “Children and motorcycles: a systematic review of risk factors and interventions”, Injury Prevention Vol. 24, No. 2.

Cercarelli, L. Rina., Arnold, P.K., Rosman, D.L., Sleet, David., y Thornett, M.L. (1992), “Travel exposure and choice of comparison crashes for examining motorcycle conspicuity by analysis of crash data”, Accident Analysis and Prevention Vol. 24, No. 4.

Chang, Li., y Wang, Hsiu (2006), “Analysis of traffic injury severity: An application of non-parametric classification tree techniques”, Accident Analysis & Prevention Vol. 38, No. 5.

Cheng, Wen., Gill, Gurdiljot., Sakrani, Taha., Dasu, Mohan., y Zhou, Jiao (2017), “Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models”, Accident Analysis and Prevention 108.

Clabaux, Nicolas., Brenac, Thierry., Perrin, Christophe., Magnin, Joël., Canu, Bastien., y Van Elslande, Pierre (2012), “Motorcyclists’ speed and ‘looked-but-failed-to-see’ accidents”, Accident Analysis and Prevention 49.

Clarke, David., Ward, Patrick., Bartle, Craig., y Truman, Wendy (2007), “The role of motorcyclist and other driver behaviour in two types of serious accident in the UK”, Accident Analysis and Prevention Vol. 39, No. 5.

Daniello, Allison y Gabler, Hampton (2011), “Fatality risk in motorcycle collisions with roadside objects in the United States”, Accident Analysis and Prevention Vol. 43, No. 3.

De Oña, Juan., Mujalli, Randa., y Calvo, Francisco (2011), “Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks”, Accident Analysis & Prevention Vol. 43, No. 1.

Delen, Dursun., Sharda, Ramesh., y Bessonov, Max (2006), “Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks”, Accident Analysis & Prevention Vol. 38, No. 3.

Delen, Dursun., Tomak, Leman., Topuz, Kazim., y Eryarsoy, Enes (2017), “Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods”, Journal of Transport & Health 4.


Donate-López, Carolina., Espigares-Rodríguez, Elena., Jiménez-Moleón, Juan., Luna-del-Castillo, Juan., Bueno-Cavanillas, Aurora., y LardelliClaret,Pablo (2010), “The association of age, sex and helmet use with the risk of death for occupants of two-wheeled motor vehicles involved in traffic crashes in Spain”, Accident Analysis & Prevention Vol. 42, No. 1.

Elliott, Mark., Baughan, Christopher., y Sexton, Barry (2007), “Errors and violations in relation to motorcyclists’ crash risk”, Accident Analysis and Prevention Vol. 39, No. 3.

Fowler, Graeme., Ray, Rose., Huang, Su-Wei., Zhao, Ke., y Frank, Todd (2016), “An examination of motorcycle antilock brake systems in reducing crash risk”, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Vol. 2, No. 2.

Haque, Md., Chin, Hoong., y Huang, Helai (2009), “Modeling fault among motorcyclists involved in crashes”, Accident Analysis and Prevention Vol. 41, No. 2.

Hashmienejad, Seyed., y Hasheminejad, Seyed (2017), “Traffic accident severity prediction using a novel multi-objective genetic algorithm”, International journal of crashworthiness Vol. 22, No. 4.

Huang, Helai y Abdel-Aty, Mohamed (2010), “Multilevel data and Bayesian analysis in traffic safety”, Accident Analysis & Prevention Vol. 42, No. 6.

Huang, Helai., Chin, Hoong., y Haque, Md (2008), “Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis”, Accident Analysis & Prevention Vol. 40, No. 1.

IDEAM (2017), “Precipitación mensual por año para Cartagena”. Recuperado de www.ideam.gov.co

IEU (2019), El mototaxismo sigue aumentando en Colombia. U. N. d. Colombia. Recuperado de http://ieu.unal.edu.co/medios/noticias-del-ieu/ item/el-mototaxismo-sigue-aumentando-en-colombia

Ivers, Rebecca., Sakashita, Chika., Senserrick, Teresa., Elkington, Jane., Lo, Serigne., Boufous, Soufiane y De Rome, Liz (2016), “Does an on-road motorcycle coaching program reduce crashes in novice riders? A randomised control trial”, Accident Analysis and Prevention 86.

Jafari, Seyed., Jahandideh, Sepideh., Jahandideh, Mina., y Asadabadi, Ebrahim (2015), “Prediction of road traffic death rate using neural networks optimised by genetic algorithm”, International journal of injury control and safety promotion Vol. 22, No. 2.

Kashani, Ali y Mohaymany, Afshin (2011), “Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models”, Safety Science Vol. 49, No. 10.

Kashani, Ali., Rabieyan, Rahim., y Besharati, Mohammad (2014), “A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers”, Journal of Safety Research 51.

Langley, John., Mullin, Bernadette., Jackson, Rodney., y Norton, Robyn (2000), “Motorcycle engine size and risk of moderate to fatal injury from a motorcycle crash”, Accident Analysis and Prevention Vol. 32, No. 5.

Li, Xiugang., Lord, Dominique., Zhang, Yunlong y Xie, Yuanchang (2008), “Predicting motor vehicle crashes using support vector machine models”, Accident Analysis & Prevention Vol. 40, No. 4.

Mannering, Fred (2018), “Temporal instability and the analysis of highway accident data”, Analytic methods in accident research 17.

Martín, Luis., Baena, Leticia., Garach, Laura., López, Griselda., y De Oña, Juan (2014), “Using data mining techniques to road safety improvement in Spanish roads”, Procedia-Social and Behavioral Sciences 160.

Moghaddam., F. Rezaie., Afandizadeh, Shariar., y Ziyadi, Max (2011), “Prediction of accident severity using artificial neural networks”, International Journal of Civil Engineering Vol. 9, No. 1.

Montella, Alfonso., Aria, Massimo., D’Ambrosio, Antonio., y Mauriello, Filomena (2012), “Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery”, Accident Analysis and Prevention 49.

ONSV (2019), “Boletín 2018 (Parcial), Fallecidos, lesionados y hechos de tránsito en Colombia por INMLCF.”

ONSV (2019), “Boletines Estadísticos, Fallecidos, Lesionados y hechos de tránsito en Colombia- Histórico Años 2012-2017.”, Recuperado de https:// ansv.gov.co/observatorio/?op=Contenidos&sec=63.

Ospina-Mateus, Holman y Quintana, Leonardo (2019), “Understanding the impact of physical fatigue and postural comfort experienced during motorcycling: A systematic review”, Journal of Transport & Health 12.

Ospina-Mateus, Holman., Quintana, Leonardo., Lopez-Valdes, Francisco., y Salas-Navarro, Katherinne (2019), “Bibliometric analysis in motorcycle accident research: a global overview”, Scientometrics Vol. 121, No. 2.

Ospina-Mateus, Holman., Jiménez, Leonardo., López-Valdés, Francisco., Morales-Londoño, Natalie., y Salas-Navarro, Katherinne (2019), Using data-mining techniques for the prediction of the severity of road crashes in Cartagena, Colombia. Workshop on Engineering Applications, Springer.

Pai, Chih (2011), “Motorcycle right-of-way accidents - A literature review”, Accident Analysis and Prevention Vol. 43, No. 3.

Peek-Asa, Corinne y Kraus, Jess (1996), “Alcohol use, driver, and crash characteristics among injured motorcycle drivers”, Journal of Trauma - Injury, Infection and Critical Care Vol. 41, No. 6.

Quddus, Mohammed., Chin, Hoong., y Wang, J. (2001), “Motorcycle crash prediction model for signalised intersections”, WIT Transactions on the Built Environment 52.

Quinlan, J. Ross (1986), “Induction of decision trees”, Machine learning Vol. 1, No. 1.

Quinlan, J. Ross (1993), C4. 5: Programming for machine learning, San Francisco, CA.: Morgan Kauffmann.

Rizzi, Matteo., Strandroth, Johan., Holst, Jan., y Tingvall, Claes (2016), “Does the improved stability offered by motorcycle antilock brakes (ABS) make sliding crashes less common? In-depth analysis of fatal crashes involving motorcycles fitted with ABS”, Traffic Injury Prevention Vol. 17, No. 6.

Sager, Bertrand., Yanko, Matthew., Spalek, .,Thomas., Froc, David., Bernstein, Daniel., y Dastur, Farhad (2014), “Motorcyclist’s lane position as a factor in right-of-way violation collisions: A driving simulator study”, Accident Analysis and Prevention 72.

Schneider IV, William., Savolainen, Peter., Van Boxel, Dan., y Beverley, Rick (2012), “Examination of factors determining fault in two-vehicle motorcycle crashes”, Accident Analysis and Prevention 45.

Schneider IV, William., Savolainen, Peter., y Moore, Darren (2010), “Effects of horizontal curvature on single-vehicle motorcycle crashes along rural twolane highways”, Transportation Research Record Vol. 2194, No. 1.

Segui-Gomez, María y Lopez-Valdes, Francisco (2007), “Recognizing the importance of injury in other policy forums: The case of motorcycle licensing policy in Spain”, Injury Prevention Vol. 13, No. 6.

Sohn, So y Shin, Hyungwon (2001), “Pattern recognition for road traffic accident severity in Korea”, Ergonomics Vol. 44, No. 1.

Taamneh, Madhar., Alkheder, Sharaf., y Taamneh, Safah (2017), “Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates”, Journal of Transportation Safety & Security Vol. 9, No. 2.

Truong, Long., Nguyen, Hang., y De Gruyter, Chris (2019), “Mobile phone use while riding a motorcycle and crashes among university students”, Traffic injury prevention Vol. 20, No. 2.

World Health Organization (WHO) (2017), Seguridad de los vehículos de motor de dos y tres ruedas: manual de seguridad vial para decisores y profesionales. Génova: World Health Organization,

WHO (2018), Global status report on road safety 2018. Génova: World Health Organization.

Witten, Ian., Frank, Eibe., Hall, Mark., y Pal, Christopher (2016), Data Mining: Practical machine learning tools and techniques, San Francisco, CA.: Morgan Kaufmann.

Xi, Jianfeng., Gao, Zhenhai., S. Niu, T. Ding y G. Ning (2013), “A hybrid algorithm of traffic accident data mining on cause analysis”, Mathematical Problems in Engineering 2013.

Zambon, Francesco y Hasselberg, Marie (2006), “Socioeconomic differences and motorcycle injuries: age at risk and injury severity among young drivers. A Swedish nationwide cohort study”, Accident Analysis and Prevention Vol. 38, No. 6.
Publicado
2019-12-01
Cómo citar
Ospina-Mateus, H., & Quintana Jiménez, L. (2019). Predicción de accidentes viales en Cartagena, Colombia, con árboles de decisión y reglas de asociación. Economía & Región, 13(2), 83-115. https://doi.org/10.32397/er.vol13.n2.3
Resumen - 898