Using Decision Trees To Extract Decision Rules From Police Reports On Road Accidents
Available online at www.sciencedirect.com
Procedia - Social and Behavioral Sciences 53 (2012) 106 – 114
SIIV - 5th International Congress - Sustainability of Road Infrastructures
Using Decision Trees to extract Decision Rules from Police Reports on Road
Accidents
López Griseldaa*, de Oña Juanb and Abellán Joaquínc
a
Ph. D. Student. TRYSE Research Group. Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa s/n, 18071, Granada, Spain a Ph. D. TRYSE Research Group. Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/ Severo
Ochoa, s/n, 18071 Granada (Spain) c Ph. D. Department of Computer Science & Artificial Intelligence, University of Granada, ETSI Informática, c/Periodista Daniel
Saucedo Aranda s/n, 18071, Granada, Spain
Abstract
The World Health Organization (WHO) considers that traffic accidents are major public health problem worldwide, for this reason safety managers try to identify the main factors affecting the severity as consequence of road accidents. In order to identify these factors, in this paper, Data Mining (DM) techniques such as Decision Trees (DTs), have been used. A dataset of traffic accidents on rural roads in the province of Granada (Spain) have been analyzed.
DTs allow certain decision rules to be extracted. These rules could be used in future road safety campaigns and would enable managers to implement certain priority actions.
© 2012
2012 The
The Authors.
Authors. Published
Published by by Elsevier
Elsevier Ltd.
Ltd. Selection and/or peer-review under responsibility of SIIV2012
©
Scientific
Open access under CC BY-NC-ND license. Scientific Committee
Selection Committee and/or peer-review under responsibility of SIIV2012
Keywords: Type your keywords here, separated by semicolons ;
1. Introduction
The World Health Organization (WHO) considers that traffic accidents are major public health problem worldwide that every year claiming 1.27 million
Procedia - Social and Behavioral Sciences 53 (2012) 106 – 114
SIIV - 5th International Congress - Sustainability of Road Infrastructures
Using Decision Trees to extract Decision Rules from Police Reports on Road
Accidents
López Griseldaa*, de Oña Juanb and Abellán Joaquínc
a
Ph. D. Student. TRYSE Research Group. Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/Severo Ochoa s/n, 18071, Granada, Spain a Ph. D. TRYSE Research Group. Department of Civil Engineering, University of Granada, ETSI Caminos, Canales y Puertos, c/ Severo
Ochoa, s/n, 18071 Granada (Spain) c Ph. D. Department of Computer Science & Artificial Intelligence, University of Granada, ETSI Informática, c/Periodista Daniel
Saucedo Aranda s/n, 18071, Granada, Spain
Abstract
The World Health Organization (WHO) considers that traffic accidents are major public health problem worldwide, for this reason safety managers try to identify the main factors affecting the severity as consequence of road accidents. In order to identify these factors, in this paper, Data Mining (DM) techniques such as Decision Trees (DTs), have been used. A dataset of traffic accidents on rural roads in the province of Granada (Spain) have been analyzed.
DTs allow certain decision rules to be extracted. These rules could be used in future road safety campaigns and would enable managers to implement certain priority actions.
© 2012
2012 The
The Authors.
Authors. Published
Published by by Elsevier
Elsevier Ltd.
Ltd. Selection and/or peer-review under responsibility of SIIV2012
©
Scientific
Open access under CC BY-NC-ND license. Scientific Committee
Selection Committee and/or peer-review under responsibility of SIIV2012
Keywords: Type your keywords here, separated by semicolons ;
1. Introduction
The World Health Organization (WHO) considers that traffic accidents are major public health problem worldwide that every year claiming 1.27 million
References: [1] WHO, World Health Organization, (2009). Informe Global sobre el estado de la Seguridad Vial: Tiempo para la Acción. Available at: www.who.int/violence_injury_prevention/road_safety_status/2009 [2] Kashani, A. and Mohaymany, A. (2011). “Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models” [3] Savolainen, P., Mannering, F., Lord, D., Quddus, M., (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives [5] Chang, L.Y. and Wang, H.W. (2006). “Analysis of traffic injury severity: an application of non-parametric classification tree techniques”. Accident Analysis and Prevention 38, pp. 1019–1027. [6] Kuhnert, P.M., Do, K.A., McClure, R., (2000). Combining non-parametric models with logistic regression: an application to motor vehicle injury data [7] Pakgohar, A., Tabrizi, R.S., Khalilli, M., Esmaeili, A., (2010). The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach [8] Kashani, A., Mohaymany, A., Ranjbari, A., (2011). A Data Mining Approach to Identify Key Factors of Traffic Injury Severity. PrometTraffic & Transportation, 23 (1), 11-17. [9] Montella, A., Aria, M., D´Ambrosio, A.,Mauriello, F., 2011. Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery [10] Breiman, L., Friedman, J.,Olshen, R., and Stone, C., (1984). “Classification and Regression Trees”. Belmont, CA: Chapman & Hall. [11] De Oña, J., Mujalli, R.O., Calvo, F.J., (2011). “Analysis of traffic accident injury on Spanish rural highways using Bayesian networks”. [12] Abdel Wahab, H.T., Abdel-Aty, M.A., (2001). Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections [13] Kockelman, K.M., Kweon, Y.J., (2002). Driver injury severity: an application of ordered probit models. Accident Analysis and Prevention 34, 313–321. [14] Al-Ghamdi, A., (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis and Prevention 34 (6), 729–741. [15] Abdel-Aty, M., (2003). “Analysis of driver injury severity levels at multiple locations using ordered probit models”. Journal of Safety Research 34, 597–603. [16] Gray, R.C., Quddus, M.A., Evans, A., (2008). “Injury severity analysis of accidents involving young male drivers in Great Britain”. [17] Helai, H., Chor, C.H., Haque, M.M., (2008). “Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis” [18] Montella, A., Aria, M., D´Ambrosio, A., Mauriello, F., (2011). Data Mining Techniques for Exploratory Analysis of Pedestrian Crashes. Transportation Research Record: Journal of Transportation Research Board, No. 2237, Transportation Research Board of the National Academies, Washington, D.C., 107-116.