A Multiobjective Particle Swarm Optimization-Based Partial Classification for Accident Severity Analysis

Reducing accident severity is an effective way to improve road safety. In this article, a novel multiobjective particle swarm optimization (MOPSO)-based partial classification method is employed to identify the contributing factors that impact accident severity. The accident dataset contains only a few fatal accidents but the patterns of fatal accidents are of great interest to traffic agencies. Partial classification can deal with the unbalanced dataset by producing rules for each class. The rules can be evaluated by several conflicting criteria such as accuracy and comprehensibility. A MOPSO is applied to discover a set of Pareto optimal rules. The accident data of Beijing between 2008 and 2010 are used to build the model. The proposed approach is compared with several rule-learning algorithms. The results show that the proposed approach can generate a set of accurate and comprehensible rules, which can indicate the relationship between risk factors and accident severity.

[1]  William W. Cohen.  Fast Effective Rule Induction , 1995, ICML.

[2]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[3]  G. Guyatt,et al.  The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. , 2002, Accident; analysis and prevention.

[4]  Kelvin K W Yau,et al.  Multiple-vehicle traffic accidents in Hong Kong. , 2006, Accident; analysis and prevention.

[5]  Fred L Mannering,et al.  Highway accident severities and the mixed logit model: an exploratory empirical analysis. , 2008, Accident; analysis and prevention.

[6]  Junjie Yang,et al.  A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO) , 2009, Comput. Math. Appl..

[7]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[8]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

[9]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[10]  Alex A. Freitas,et al.  A Genetic Algorithm for Generalized Rule Induction , 1999 .

[11]  Erhan Akin,et al.  Multi-objective rule mining using a chaotic particle swarm optimization algorithm , 2009, Knowl. Based Syst..

[12]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[13]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[14]  Ramesh Sharda,et al.  Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. , 2006, Accident; analysis and prevention.

[15]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[16]  Beatriz de la Iglesia,et al.  A multi-objective GRASP for partial classification , 2008, Soft Comput..

[17]  Kamal Ali,et al.  Partial Classification Using Association Rules , 1997, KDD.

[18]  Tibebe Beshah,et al.  Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia , 2010, AAAI Spring Symposium: Artificial Intelligence for Development.

[19]  Rajib Mall,et al.  Predictive and comprehensible rule discovery using a multi-objective genetic algorithm , 2006, Knowl. Based Syst..

[20]  Toshiyuki Yamamoto,et al.  Bivariate ordered-response probit model of driver's and passenger's injury severities in collisions with fixed objects. , 2004, Accident; analysis and prevention.

[21]  A. S. Al-Ghamdi.  Using logistic regression to estimate the influence of accident factors on accident severity. , 2002, Accident; analysis and prevention.

[22]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[23]  Young-Jun Kweon,et al.  Driver injury severity: an application of ordered probit models. , 2002, Accident; analysis and prevention.

[24]  Juan de Oña,et al.  Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. , 2011, Accident; analysis and prevention.

[25]  Victor J. Rayward-Smith,et al.  The application and effectiveness of a multi-objective metaheuristic algorithm for partial classification , 2006, Eur. J. Oper. Res..

[26]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).