Variable Efficiency Appraisal in Freeway Accidents Using Artificial Neural Networks—Case Study

Traffic accident is one the most substantial concern of road safety. Prediction models are being used for defining relations between freeway accidents and effective parameters such as traffic volume, road geometric designs and environmental issues. In this research work, artificial neural network and log-normal models have been proposed to estimate the number of road accidents in Tehran-Qom freeway. Average daily traffic volume, percentage of heavy vehicle, average speed and pavement condition index are considered as input variables. Three-year period of accident data and parameters, have been used in analytical process modeling and validation. Efficiency ranking of variables in artificial neural networks and log-normal regression extracted based on the coefficients of determination. Results show that average speed of vehicles and average daily traffic volume are the most effective parameters in freeway accidents as well as artificial neural network model is more capable to estimate the number of road accidents in freeways.

[1]  Erdem Doan,et al.  An application of modified Smeed, adapted Andreassen and artificial neural network accident models to three metropolitan cities of Turkey , 2009 .

[2]  T Olmstead,et al.  Freeway management systems and motor vehicle crashes: a case study of Phoenix, Arizona. , 2001, Accident; analysis and prevention.

[3]  Thomas F. Golob,et al.  Safety Aspects of Freeway Weaving Sections , 2003 .

[4]  Stephane Hess,et al.  Effects of Speed Limit Enforcement Cameras on Accident Rates , 2003 .

[5]  J. Bared,et al.  Accident Models for Two-Lane Rural Segments and Intersections , 1998 .

[6]  Bhagwant Persaud,et al.  ACCIDENT PREDICTION MODELS FOR FREEWAYS , 1993 .

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

[8]  T. Golob,et al.  A Method for Relating Type of Crash to Traffic Flow Characteristics on Urban Freeways , 2002 .

[9]  Dominique Lord,et al.  Modeling crash-flow-density and crash-flow-V/C ratio relationships for rural and urban freeway segments. , 2005, Accident; analysis and prevention.

[10]  Mohamed A. Abdel-Aty,et al.  Calibrating a real-time traffic crash-prediction model using archived weather and ITS traffic data , 2006, IEEE Transactions on Intelligent Transportation Systems.

[11]  Hyunho Chang,et al.  AN EFFECT OF TRIP LENGTH ON FEEWAY ACCIDENT RATES , 2005 .

[12]  Neslihan Karsli,et al.  Modeling of monthly traffic accidents with the artificial neural network method , 2011 .

[13]  Linyu Xu,et al.  Water Quality Analysis of the Songhua River Basin Using Multivariate Techniques , 2009 .

[14]  Cheol Oh,et al.  Relationship between V/C and Accident Rate for Freeway Facility Sections (focused on Shingal-Ansan Freeway) , 1999 .

[15]  Abbas Mahmoudabadi,et al.  Comparison of Weighted and Simple Linear Regression and Artificial Neural Network Models in Freeway Accidents Prediction , 2010, 2010 Second International Conference on Computer and Network Technology.

[16]  S. Figen Kalyoncuoglu,et al.  An alternative approach for modelling and simulation of traffic data: artificial neural networks , 2004, Simul. Model. Pract. Theory.