Book ChapterDOI
Analysis of Driver Injury Severity in Metropolitan Roads of India Through Classification Tree
Sathish Kumar Sivasankaran,Venkatesh Balasubramanian +1 more
- pp 123-131
TLDR
Analysis of traffic crash data of single lane two way roads of Chennai city pertaining to period from January 2015 to December 2016 indicated that collision type and vehicle type were the two important variables affecting the severity of injury.Abstract:
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.read more
References
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Book
World Report on Road Traffic Injury Prevention
TL;DR: This paper is a synopsis of a major report by the WHO which collates information on crashes worldwide and summarises the key findings and the recommendations of the report.
Journal ArticleDOI
Using logistic regression to estimate the influence of accident factors on accident severity
TL;DR: The findings show that logistic regression as used in this research is a promising tool in providing meaningful interpretations that can be used for future safety improvements in Riyadh.
Journal ArticleDOI
Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks
TL;DR: Bayesian Networks (BNs) are used to identify the main factors involved in accident severity for both, the entire database (EDB) and the clusters previously obtained by LCC and the results show that the combined use of both techniques is very interesting as it reveals further information that would not have been obtained without prior segmentation of the data.
Journal ArticleDOI
Analysis of the traffic injury severity on two-lane, two-way rural roads based on classification tree models
TL;DR: The results indicated that improper overtaking and not using a seatbelt are the most important factors affecting the severity of injuries on two-lane, two-way rural roads in Iran.
Journal ArticleDOI
Data mining a diabetic data warehouse
TL;DR: This work examined one such diabetic data warehouse from a large integrated health care system in the New Orleans area with 30,383 diabetic patients, and used the classification tree approach in Classification and Regression Trees (CART) with a binary target variable of HgbA1c >9.5.
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