Severity analysis of powered two wheeler traffic accidents in Uttarakhand, India

Abstract

Objective Powered Two Wheeler (PTW) vehicles are one of the preferred modes of transport used in India. Also, PTWs accidents are comparatively more frequent than other type of accidents on road. The influencing factors of PTW accidents are also differ from factors that affect other accident types. The objective of this study is to analyze newly available PTWs road accident data from Uttarakhand state in India and revealing the factors that affect the severity of these accidents in various districts of Uttarakhand.. Methodology To analyze the factors that affect the severity of road accidents in Uttarakhand, initially we have compared three popular classification algorithms i.e. decision tree (CART), Naïve Bayes and Support vector machine on PTW accident data set. The decision tree algorithm's (CART) classification accuracy was found better than other two techniques. Hence we have preferred CART algorithm to extract the factors that affect the severity of PTWVs accidents in whole Uttarakhand state and its 13 districts separately. Experimental Results The analysis of PTWVs accident data using CART for 13 districts of Uttarakhand and the whole state reveals that every districts have different factors associated with PTW accidents severity. There are some districts in Uttarakhand state which have similar PTW accident patterns, whereas few districts are found to have different PTW accident patterns. These results are very useful to understand the pattern of PTW accidents in Uttarakhand state. These results can certainly be helpful to overcome the PTWs accident rate in Uttarakhand state.

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

“…Recently, several studies employed machine learning techniques to analyze and predict the crash severity of motorcycle crashes [26,29,31,67]. Anvari et al .…”

“…To address these drawbacks of statistical models, in this research machine learning based algorithms is proposed to predict motorcycle crash severity. In recent years, machine learning techniques have become contemporary methods in transportation safety research to identify the significant factors associated with crash severity [29–31]. They quickly explain the complex patterns associated with crash risk [32–35].…”

An improved approach for association rule mining using a multi-criteria decision support system: a case study in road safety

“…There is a rich literature that describes the different techniques and their outcomes in road accident analysis [4,6,15,23,34,35]. These techniques have found an association between drivers' behaviors, weather conditions, light conditions and the severity of accidents.…”

ANN based short-term traffic flow forecasting in undivided two lane highway

“…Kalman filtering [6], local linear regression [7], neural network [8] and fuzzy logic based models [9] are some of the methods used for the short term traffic flow prediction. Due to stochastic and highly non-linear behavior of traffic stream, machine learning techniques [10] have received a great attention and hence are taken as an alternative for traffic flow prediction. Dougherty and Cobbett [11] used back propagation neural network to develop a model to predict traffic flow, speed and traffic occupancy in the Utrecht/Rotterdam/Hague region of The Netherlands.…”