Analysis of Driver Injury Severity in Metropolitan Roads of India Through Classification Tree

  • Sathish Kumar Sivasankaran
  • Venkatesh BalasubramanianEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 823)


Reducing the injury severity from traffic accidents is most important step in mitigating accidents occurring in developing economies like India where two way roads are more common in cities. The number of deaths due to accidents has rose from 83,491 in 2005 to 1,36,071 in 2016 as per the latest reports of ministry of road transport and highways, government of India (MoRTH, 2016). To explore the factors contributing to injury severity in such roads, non parametric classification tree is used since it does not assume any underlying assumption between target variable and the predictors. CART (Classification and Regression tree), a classification tree establishes empirical relation between injury severity outcomes and variables including driver, vehicle, crash and environmental factors. The present study analyzed traffic crash data of single lane two way roads of Chennai city pertaining to period from January 2015 to December 2016. The final dataset included a total of 5271 crash information after excluding incomplete and missing data. This finalized dataset was split into two subsets, training and testing data and the classification models reported an accuracies of 63.4% and 61.5% for the training and testing data. The results indicated that collision type and vehicle type were the two important variables affecting the severity of injury. The findings of this study will help in determining influential factors so that countermeasures to reduce the severity of injury in urban cities can be developed.


Road traffic accidents Decision tree Injury severity Data mining Transportation safety 


  1. 1.
    WHO (2008) World report on road traffic injury prevention. World Health OrganizationGoogle Scholar
  2. 2.
    Road accidents in India 2012–2016, Ministry of Road Transport and highways (MoRTH), Transport Research Wing, Government of IndiaGoogle Scholar
  3. 3.
    Al-Ghamdi AS (2002) Using logistic regression to estimate the influence of accident factors on accident severity. Accid Anal Prev 34(6):729–741CrossRefGoogle Scholar
  4. 4.
    Breault JL, Goodall CR, Fos PJ (2002) Data mining a diabetic data warehouse. Artif Intell Med 26(1):37–54CrossRefGoogle Scholar
  5. 5.
    Kashani AT, Mohaymany AS (2011) Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models. Saf Sci 49(10):1314–1320CrossRefGoogle Scholar
  6. 6.
    Kashani AT, Rabieyan R, Besharati MM (2014) A data mining approach to investigate the factors influencing the crash severity of motorcycle pillion passengers. J Saf Res 51:93–98CrossRefGoogle Scholar
  7. 7.
    de Ona J, López G, Mujalli R, Calvo FJ (2013) Analysis of traffic crashes on rural highways using Latent Class Clustering and Bayesian Networks. Accid Anal Prev 51:1–10CrossRefGoogle Scholar
  8. 8.
    Montella A, Aria M, D’Ambrosio A, Mauriello F (2012) Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accid Anal Prev 49:58–72CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sathish Kumar Sivasankaran
    • 1
  • Venkatesh Balasubramanian
    • 1
    Email author
  1. 1.RBG Lab, Department of Engineering DesignIIT MadrasChennaiIndia

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