Recognition of effective factors on speed violation by using classification and regression tree model (case study: intercity roads in Isfahan province)

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Document Type : Original Article

Authors

1 Civil engineering department, Isfahan university of Technology

2 Faculty of Transportation engineering, Isfahan university of Technology, Isfahan, Iran

3 Faculty of civil engineering, Isfahan university of Technology, Isfahan, Iran

4 Civil Engineering department, Iran university of Science & Technology

Abstract

Usually indicators like number of crashes and possible costs related to it are considered for safety assessment while those indicators are not controller and have no effects on safety and sometimes this information are not accessible for understudied area. Therefore, other safety indicators are required for safety control and supervisory which are called surrogate indicators. Vehicle speed is a key factor in road traffic safety. Theme of current paper is identifying effective factors on speed violation as surrogate indicator in intercity roads of Isfahan province by using classification and regression tree (CART) model. Information of vehicle speed and violating road speed limit in 10 intercity roads axis of Isfahan province are gathered by using Robot speed cameras. Also 4 main category of factors affecting speed violation including human, road, vehicle and environmental factors of those roads axis are gathered. speed violation model has 74.7% of total accuracy. Results indicated that among 4 main category of factors affecting speed violation, human factors have the most impact on speed violations. Among these human factors, frequency and duration of rests, driver experience level, travel time, driver literacy level, talking on phone while driving and presence of second driver in vehicle have more importance in the model classification improvement. After human factors, road factors are more important than other factors in classification of speed violation in which number of attractive points and volume have the most impact. In contrast to above mentioned factors, vehicle related factors shows the least importance to model classification.

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