Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia
Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
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- oai:repositorio.utb.edu.co:20.500.12585/9195
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/9195
- Palabra clave:
- Data mining
Prediction
Road crashes
Severity
Decision trees
Forecasting
Highway accidents
Motor transportation
Motorcycles
Roads and streets
Support vector machines
Area under the ROC curve
Classification algorithm
Knowledge analysis
Machine learning techniques
Multi layer perceptron
Road crash
Severity
Support vector machine (SVMs)
Data mining
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| title |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| spellingShingle |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia Data mining Prediction Road crashes Severity Decision trees Forecasting Highway accidents Motor transportation Motorcycles Roads and streets Support vector machines Area under the ROC curve Classification algorithm Knowledge analysis Machine learning techniques Multi layer perceptron Road crash Severity Support vector machine (SVMs) Data mining |
| title_short |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| title_full |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| title_fullStr |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| title_full_unstemmed |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| title_sort |
Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombia |
| dc.contributor.editor.none.fl_str_mv |
Figueroa-Garcia J.C. Duarte-Gonzalez M. Jaramillo-Isaza S. Orjuela-Canon A.D. Diaz-Gutierrez Y. |
| dc.subject.keywords.none.fl_str_mv |
Data mining Prediction Road crashes Severity Decision trees Forecasting Highway accidents Motor transportation Motorcycles Roads and streets Support vector machines Area under the ROC curve Classification algorithm Knowledge analysis Machine learning techniques Multi layer perceptron Road crash Severity Support vector machine (SVMs) Data mining |
| topic |
Data mining Prediction Road crashes Severity Decision trees Forecasting Highway accidents Motor transportation Motorcycles Roads and streets Support vector machines Area under the ROC curve Classification algorithm Knowledge analysis Machine learning techniques Multi layer perceptron Road crash Severity Support vector machine (SVMs) Data mining |
| description |
Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low. © 2019, Springer Nature Switzerland AG. |
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2019 |
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2019 |
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2020-03-26T16:33:11Z |
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2020-03-26T16:33:11Z |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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Conferencia |
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Communications in Computer and Information Science; Vol. 1052, pp. 309-320 |
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9783030310189 |
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18650929 |
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https://hdl.handle.net/20.500.12585/9195 |
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10.1007/978-3-030-31019-6_27 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Communications in Computer and Information Science; Vol. 1052, pp. 309-320 9783030310189 18650929 10.1007/978-3-030-31019-6_27 Universidad Tecnológica de Bolívar Repositorio UTB 57194034904 57195939566 23110963500 57195913974 57193504630 |
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https://hdl.handle.net/20.500.12585/9195 |
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eng |
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eng |
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16 October 2019 through 18 October 2019 |
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Recurso electrónico |
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application/pdf |
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Springer |
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Springer |
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6th Workshop on Engineering Applications, WEA 2019 |
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Figueroa-Garcia J.C.Duarte-Gonzalez M.Jaramillo-Isaza S.Orjuela-Canon A.D.Diaz-Gutierrez Y.Ospina-Mateus H.Quintana Jiménez, Leonardo AugustoLópez-Valdés F.J.Morales-Londoño N.Salas-Navarro K.2020-03-26T16:33:11Z2020-03-26T16:33:11Z2019Communications in Computer and Information Science; Vol. 1052, pp. 309-320978303031018918650929https://hdl.handle.net/20.500.12585/919510.1007/978-3-030-31019-6_27Universidad Tecnológica de BolívarRepositorio UTB5719403490457195939566231109635005719591397457193504630Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low. © 2019, Springer Nature Switzerland AG.for first author was covered by (CEIBA)?Gobernaci?n de Bol?var (Colombia). We thank the Administrative Department of Traffic and Transportation (DATT) in the accompaniment and support of the information required for this investigation.Recurso electrónicoapplication/pdfengSpringerhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075691310&doi=10.1007%2f978-3-030-31019-6_27&partnerID=40&md5=a518350f9def3052c1bbed88065c0e3f6th Workshop on Engineering Applications, WEA 2019Using Data-Mining Techniques for the Prediction of the Severity of Road Crashes in Cartagena, Colombiainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fData miningPredictionRoad crashesSeverityDecision treesForecastingHighway accidentsMotor transportationMotorcyclesRoads and streetsSupport vector machinesArea under the ROC curveClassification algorithmKnowledge analysisMachine learning techniquesMulti layer perceptronRoad crashSeveritySupport vector machine (SVMs)Data mining16 October 2019 through 18 October 2019(2018) Global Status Report on Road Safety 2018, , https://apps.who.int/iris/bitstream/handle/10665/276462/9789241565684-eng.pdf?ua=1Savolainen, P., Mannering, F., Probabilistic models of motorcyclists’ injury severities in single-and multi-vehicle crashes (2007) In English). Accid. Anal. Prev., 39 (5), pp. 955-963Abdelwahab, H., Abdel-Aty, M., Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (2001) Transp. Res. Rec.: J. Transp. Res. Board, 1746, pp. 6-13Hashmienejad, S.H.-A., Hasheminejad, S.M.H., Traffic accident severity prediction using a novel multi-objective genetic algorithm (2017) Int. J. Crashworthiness, 22 (4), pp. 425-440Sohn, S., Shin, H., Data mining for road traffic accident type classification (2001) Ergonomics, 44, pp. 107-117Huang, H., Abdel-Aty, M., Multilevel data and Bayesian analysis in traffic safety (2010) Accid. Anal. Prev., 42 (6), pp. 1556-1565Li, Z., Liu, P., Wang, W., Xu, C., Using support vector machine models for crash injury severity analysis (2012) Accid. Anal. Prev., 45, pp. 478-486Delen, D., Tomak, L., Topuz, K., Eryarsoy, E., Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods (2017) J. Transp. Health, 4, pp. 118-131Balasubramanian, V., Jagannath, M., Detecting motorcycle rider local physical fatigue and discomfort using surface electromyography and seat interface pressure (2014) Transp. Res. Part F, 22, pp. 150-158Shafiei, U.K.M., Karuppiah, K., Tmrin, S.B.M., Meng, G.Y., Rasdi, I., Alias, A.N., The effectiveness of new model of motorcycle seat with built-in lumbar support (2015) Jurnal Teknologi, 77 (27), pp. 97-103. , in EnglishOspina-Mateus, H., Jiménez, L.A.Q., Understanding the impact of physical fatigue and postural comfort experienced during motorcycling: A systematic review (2019) J. Transp. Health, 12, pp. 290-318(2017) Seguridad De Los vehículos De Motor De Dos Y Tres Ruedas: Manual De Seguridad Vial Para Decisores Y Profesionales, , https://apps.who.int/iris/bitstream/handle/10665/272757/9789243511924-spa.pdf?sequence=1&isAllowed=ySegui-Gomez, M., Lopez-Valdes, F.J., Recognizing the importance of injury in other policy forums: The case of motorcycle licensing policy in Spain (2007) Inj. Prev. Short Surv., 13 (6), pp. 429-430. , in EnglishSchneider Iv, W.H., Savolainen, P.T., van Boxel, D., Beverley, R., Examination of factors determining fault in two-vehicle motorcycle crashes (2012) In English). Accid. Anal. Prev., 45, pp. 669-676Ivers, R.Q., Does an on-road motorcycle coaching program reduce crashes in novice riders? A randomised control trial (in English) (2016) Accid. Anal. Prev., 86, pp. 40-46Donate-López, C., Espigares-Rodríguez, E., Jiménez-Moleón, J.J., De Dios Luna-del-Castillo, J., Bueno-Cavanillas, A., Lardelli-Claret, P.: 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 (2010) Accid. Anal. Prev, 42 (1), pp. 297-306Albalate, D., Fernández-Villadangos, L., Motorcycle injury severity in Barcelona: The role of vehicle type and congestion (2010) In English). Traffic Inj. Prev., 11 (6), pp. 623-631Clabaux, N., Brenac, T., Perrin, C., Magnin, J., Canu, B., van Elslande, P., Motorcyclists’ speed and “looked-but-failed-to-see” accidents (2012) In English). Accid. Anal. Prev., 49, pp. 73-77Sager, B., Yanko, M.R., Spalek, T.M., Froc, D.J., Bernstein, D.M., Dastur, F.N., Motorcyclist’s lane position as a factor in right-of-way violation collisions: A driving simulator study (2014) In English). Accid. Anal. Prev., 72, pp. 325-329Rizzi, M., Strandroth, J., Holst, J., Tingvall, C., 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 (2016) In English). Traffic Inj. Prev., 17 (6), pp. 625-632Clarke, D.D., Ward, P., Bartle, C., Truman, W., The role of motorcyclist and other driver behaviour in two types of serious accident in the UK (2007) In English). Accid. Anal. Prev., 39 (5), pp. 974-981López-Valdés, F.J., García, D., Pedrero, D., Moreno, J.L., Accidents of motorcyclists against roadside infrastructure (2005) IUTAM Symposium on Impact Biomechanics: From Fundamental Insights to Applications, 124, pp. 163-170. , vol., pp., DublinBrown, J., Schonstein, L., Ivers, R., Keay, L., Children and motorcycles: A systematic review of risk factors and interventions (2018) Inj. Prev., 24 (2), pp. 166-175Elliott, M.A., Baughan, C.J., Sexton, B.F., Errors and violations in relation to motorcyclists’ crash risk (In English) (2007) Accid. Anal. Prev., 39 (3), pp. 491-499Truong, L.T., Nguyen, H.T., de Gruyter, C., Mobile phone use while riding a motorcycle and crashes among university students (2019) Traffic Inj. Prev., 20, pp. 1-7http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/9195/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/9195oai:repositorio.utb.edu.co:20.500.12585/91952023-04-24 09:29:21.531Repositorio Institucional UTBrepositorioutb@utb.edu.co |