Prediction and Investigation of Road Traffic Accident Severity Factors using Support Vector Machine Algorithm

Authors

1 Faculty of Civil Engineering, Yazd University

2 Department of Industrial Engineering, Yazd University

Abstract

Accidents as a threat factor to the transport system have widespread political, social, and economic dimensions, which is increasing in developing countries. Iran, as a developing country, has not escaped this danger, but in recent years it has taken preventive measures and crash statistics has been decreasing. Crash injury severity is one of the most important criterions of measuring the costs of accidents. Different methods have been used for predict modeling. Support Vector Machine is a relatively new modeling technique which was proposed to solve the classification and regression problems. That shows accurate and acceptable performance. In the present study, it has been tried to model the severity of accidents with a combination clustering and classification approach, with the help of neural network algorithms, simple parsing, SVM, KNN, and C4.5 algorithms and by comparing the algorithms used assess the ability of each of the algorithms in the prediction of the severity of accidents. The purpose of this study is to evaluate the performance of support vector machine algorithm to predict the severity of road accidents and identify the factors that affect the severity of accidents.

Keywords

References

اردکانی ح، حاتمی ع، نجف پ، "بررسی تأثیر جنسیت رانندگان در عوامل مؤثر بر شدت تصادفات درون شهری"، یازدهمین کنفرانس بین­المللی مهندسی حمل و نقل و ترافیک، تهران، 1390.
معین­الدینی ا، حبیبیان م، "بررسی عوامل مؤثر بر شدت جراحت حادثه دیدگان در تصادفات کامیون­ها"، پانزدهمین کنفرانس بین­المللی مهندسی حمل و نقل و ترافیک، 1394.
Abdel-Aty M, Yu R, “Analyzing crash injury severity for a mountainous freeway incorporating real-time traffic and weather data”, Safety Science, 2013.
Aghayan I, Hadji Hosseinlou M, Kunt M, “Application of Support Vector Machine for Crash Injury Severity Prediction: A Model Comparison Approach”, Journal of Civil Engineering and Urbanism, 2015.
Chen C, Zhang G, Qian Z, Rafiqul A, Tian Z, “Investigating driver injury severity patterns in rollover crashes using support vector machine models”, Accident Analysis and Prevention, 2016.
Chen S, Wang W, Van Z, “Construct support vector machine ensemble to detect traffic incident”, Expert Systems with Applications, 2009.
Chong M, Abraham A, Paprzicky M, “Traffic Accident Data Mining Using Machine Learning Paradigms”, Informatica, 2005.
Dong N, Huang H, Zheng L, “Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects”, Accident Analysis and Prevention, 2015.
Li X, Lord D, Zhang Y, “Predicting Motor Vehicle Crashes Using Support Vector Machine Models”, Accident Analysis and Prevention, 2008.
Martín L, Baena L, Garach L, López G, Oña J, “Using data mining techniques to road safety improvement in Spanish roads”, Procedia- Social and Behavioral Sciences, 2014.
Moghaddam F, Afandizadeh Sh, Ziyari M, “Prediction of Accident Severity Using Artificial Neural Networks”, International Journal of Civil Engineering. 2011, 9 (1), 41-49.
Ren G, Zhou Z, “Traffic safety forecasting method by particle swarm optimization and support vector machine”, Expert Systems with Applications, 2011.
Yuan F, Cheu R, “Incident detection using support vector machines”, Transportation Research Part C. 2003.
Zhibin L, Pan L, Wang Wei, Chengcheng Xu, “Using support vector machine models for crash injury severity analysis”, Accident Analysis and Prevention, 2012, 478-486.
Journal of Civil and Environmental Engineering
Volume 49.3, Issue 96 - Serial Number 96
Autumn, 2019
December 2019
Pages 35-43

Files

Share

How to cite

Statistics