Application of Adaptive Neuro-fuzzy Inference System for road accident prediction

Since the last two decades, several modeling approaches have been developed in road safety literature to establish the relationship between traffic accidents and road characteristics. However, to the best of the authors’ knowledge, no extensive research work has been published on application of Adaptive Neuro-fuzzy Inference System (ANFIS) on road accident modelling. Therefore, the present paper aims to develop an ANFIS technique for modelling traffic accidents as a function of road and roadside characteristics. To achieve the objective, accident data and road characteristics were collected over a two-year period along the Qazvin-Loshan intercity roadway in Iran. The candidate set of explanatory variables included the Mean Horizontal Curvature (MHC), Shoulder Width (SW), Road Width (RW), Land Use (LU), Access Points (AP), Longitudinal Grade (LG), and Horizontal Curve Density (HCD). The results showed that RW, SW, LU, and AP significantly affected accident frequencies. Using statistical performance indices, the ANFIS model was compared with the Poisson, negative binomial, and non-linear exponential regression models. Based on the comparative results, the proposed model had higher prediction performance than the other three traditional models which has been widely used in the literature. To conclude, the proposed model could be used as a robust approach to handle uncertainty and complexity existed in accident data. In general, ANFIS model can be an effective tool for transportation agencies since intervention decisions and plans aiming at improving road safety depend on the prediction capabilities of a system.

[1]  Chen Zhang,et al.  Effects of Geometric Characteristics on Head-On Crash Incidence on Two-Lane Roads in Connecticut , 2005 .

[2]  Yu-Chiun Chiou,et al.  An artificial neural network-based expert system for the appraisal of two-car crash accidents. , 2006, Accident; analysis and prevention.

[3]  Seiichi Kagaya,et al.  Development of Transport Mode Choice Model by Using Adaptive Neuro-Fuzzy Inference System , 2006 .

[4]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[5]  Hojjat Adeli,et al.  FUZZY-WAVELET RBFNN MODEL FOR FREEWAY INCIDENT DETECTION , 2000 .

[6]  Dominique Lord,et al.  Application of finite mixture models for vehicle crash data analysis. , 2009, Accident; analysis and prevention.

[7]  Liping Fu,et al.  A Comparative Study of Alternative Model Structures and Criteria for Ranking Locations for Safety Improvements , 2006 .

[8]  M. Viswanathan,et al.  Neuro-fuzzy Learning for Automated Incident Detection , 2006, IEA/AIE.

[9]  Xiugang Li,et al.  Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.

[10]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[11]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[12]  J.-S.R. Jang,et al.  Input selection for ANFIS learning , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[13]  J. Hilbe.  Negative Binomial Regression: Preface , 2007 .

[14]  Fred L. Mannering,et al.  The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies , 1998 .

[15]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[16]  Tarek Sayed,et al.  Identifying Accident-Prone Locations Using Fuzzy Pattern Recognition , 1995 .

[17]  Mojtaba Ahmadi,et al.  An Adaptive Neuro-Fuzzy Inference System for estimating the number of vehicles for queue management at signalized intersections , 2011 .

[18]  Konstadinos G. Goulias,et al.  Application of Adaptive Neuro-Fuzzy Inference System to Analysis of Travel Behavior , 2003 .

[19]  Srinivas Reddy Geedipally,et al.  Application of the Conway-Maxwell-Poisson generalized linear model for analyzing motor vehicle crashes. , 2008, Accident; analysis and prevention.

[20]  Fakhri Karray,et al.  Inferring operating rules for reservoir operations using fuzzy regression and ANFIS , 2007, Fuzzy Sets Syst..

[21]  L Mussone,et al.  An analysis of urban collisions using an artificial intelligence model. , 1999, Accident; analysis and prevention.

[22]  Hoong Chor Chin,et al.  Identification of Accident Causal Factors and Prediction of Hazardousness of Intersection Approaches , 2003 .

[23]  Ali Payıdar Akgüngör,et al.  An artificial intelligent approach to traffic accident estimation: Model development and application , 2009 .

[24]  Gopal R. Patil,et al.  Modelling Gap Acceptance Behavior of Two-Wheelers at Uncontrolled Intersection Using Neuro-Fuzzy , 2011 .

[25]  Poul Greibe,et al.  Accident prediction models for urban roads. , 2003, Accident; analysis and prevention.

[26]  Yuanchang Xie,et al.  Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis. , 2007, Accident; analysis and prevention.

[27]  Simon Washington,et al.  Modeling crash types: New insights into the effects of covariates on crashes at rural intersections , 2006 .

[28]  N Schretter,et al.  A FUZZY LOGIC EXPERT SYSTEM FOR DETERMINING THE REQUIRED WAITING PERIOD AFTER TRAFFIC ACCIDENTS , 1996 .

[29]  Jean-Louis Martin,et al.  Relationship between crash rate and hourly traffic flow on interurban motorways. , 2002, Accident; analysis and prevention.

[30]  Nadir Yayla,et al.  The modeling of mode choices of intercity freight transportation with the artificial neural networks and adaptive neuro-fuzzy inference system , 2009, Expert Syst. Appl..

[31]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[32]  H Lum,et al.  Modeling vehicle accidents and highway geometric design relationships. , 1993, Accident; analysis and prevention.

[33]  Bruce N. Janson,et al.  Prediction Models for Truck Accidents at Freeway Ramps in Washington State Using Regression and Artificial Intelligence Techniques , 1998 .

[34]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[35]  Tarek Sayed,et al.  Urban Arterial Accident Prediction Models with Spatial Effects , 2009 .

[36]  Octavio Gómez Dantés,et al.  The World Health Report 1999. Making a Difference , 1999 .

[37]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[38]  B Arenas Ramírez,et al.  The influence of heavy goods vehicle traffic on accidents on different types of Spanish interurban roads. , 2009, Accident; analysis and prevention.

[39]  Geert Wets,et al.  A Bayesian model for ranking hazardous road sites , 2007 .

[40]  M J Maher,et al.  A comprehensive methodology for the fitting of predictive accident models. , 1996, Accident; analysis and prevention.

[41]  Tarek Sayed,et al.  Development of a Road Safety Risk Index , 2002 .

[42]  Mohamed Abdel-Aty,et al.  Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections , 2001 .

[43]  Simon Washington,et al.  On the nature of over-dispersion in motor vehicle crash prediction models. , 2007, Accident; analysis and prevention.

[44]  Kay Fitzpatrick,et al.  Evaluating the Effects of Freeway Design Elements on Safety , 2010 .

[45]  Murad Samhouri,et al.  Projection of future transport energy demand of Jordan using adaptive neuro-fuzzy technique , 2012 .

[46]  Alfonso Montella,et al.  Crash Prediction Models for Rural Motorways , 2008 .