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Identifying the Most Important Factors in the At-Fault Probability of Motorcyclists by Data Mining, Based on Classification Tree Models

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Abstract

The motorcycle is considered one of the most applicable transportation modes for various trips in Iran. According to a 2011 report by Iran’s Police Department, motorcyclists and their passengers accounted for almost 25% of all crash fatalities. The objective of this study is to identify the most important factors that contribute to the fault of motorcyclists involved in crashes. The classification and regression trees model is used in this research to differentiate between at-fault and not-at-fault cases. The results show collision type to be the most determining factor for at-fault probability of motorcyclists. According to this fact, the probability of rear-end collision is the highest, while the probability of side collisions is the lowest. Other factors involved vary according to the collision type. Factors affecting rear-end collisions the most are passenger characteristics and the rider’s age. However, side collisions are mainly due to lighting conditions and area types (urban and rural roads). Finally, this paper suggests that training riders and installing warning systems that warn drivers when they are too close to motorcycles can reduce rear-end and side collisions to a great extent.

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Authors and Affiliations

  1. School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

    Mohammad Bagher Anvari, Ali Tavakoli Kashani & Rahim Rabieyan

Authors
  1. Mohammad Bagher Anvari
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  2. Ali Tavakoli Kashani
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  3. Rahim Rabieyan
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Correspondence to Ali Tavakoli Kashani.

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Anvari, M.B., Tavakoli Kashani, A. & Rabieyan, R. Identifying the Most Important Factors in the At-Fault Probability of Motorcyclists by Data Mining, Based on Classification Tree Models. Int J Civ Eng 15, 653–662 (2017). https://doi.org/10.1007/s40999-017-0180-0

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  • DOI: https://doi.org/10.1007/s40999-017-0180-0

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