Elsevier

Accident Analysis & Prevention

Volume 87, February 2016, Pages 17-33
Accident Analysis & Prevention

A novel framework for improvement of road accidents considering decision-making styles of drivers in a large metropolitan area

https://doi.org/10.1016/j.aap.2015.11.007Get rights and content

Highlights

  • The objective of this paper is modeling and improvement of road accidents factors.

  • Injury severity and decision-making styles of drivers are considered.

  • A novel framework is proposed based on DEA and statistical methods.

  • The actual data of more than 500 drivers in Tehran, Iran is used.

  • Results indicate that flexible decision style is dominant for all accident injuries.

Abstract

Road accidents can be caused by different factors such as human factors. Quality of the decision-making process of drivers could have a considerable impact on preventing disasters. The main objective of this study is the analysis of factors affecting road accidents by considering the severity of accidents and decision-making styles of drivers. To this end, a novel framework is proposed based on data envelopment analysis (DEA) and statistical methods (SMs) to assess the factors affecting road accidents. In this study, for the first time, dominant decision-making styles of drivers with respect to severity of injuries are identified. To show the applicability of the proposed framework, this research employs actual data of more than 500 samples in Tehran, Iran. The empirical results indicate that the flexible decision style is the dominant style for both minor and severe levels of accident injuries.

Section snippets

Motivation and significance

Decision making is the selection of a procedure to weigh alternatives and find a solution for a problem. Generally, people differ in their approach to making decision, which is named their decision making style. The driver's decision-making style as an important human characteristic could considerably reduce the severity of road accidents. Therefore, the major motivation behind this study is to assess quantitative factors affecting road accidents considering decision-making styles and injury

Literature review

Identifying the factors which significantly affect the injury severity of traffic accidents was the main objective of many previous studies. These factors are usually caused by one or more of the following factors: human (driver), vehicle, road and environment characteristics (Clarke and Adams, 1990, Wang et al., 2002, Dissanayake, 2004, Vaez and Laflamme, 2005, Yannis et al., 2005, Koushki and Bustan, 2006, Vorko-Jović et al., 2006, Gray et al., 2008, De Oña et al., 2011, Lambert-Bélanger et

Methodology: the framework

In this study, a novel framework to analyze and assess the decision styles of the drivers and the factors affecting road accidents is proposed. The flowchart of the proposed model is shown in Fig. 1. The framework includes four main phases, which are identified below:

  • Phase 1:

    • Identifying the factors affecting road accidents based on literature review.

    • Designing an appropriate questionnaire (first questionnaire) based on the identified factors to determine the impact degree of each factor in the

Results and discussion

In order to investigate the factors affecting road accidents, 528 drivers are surveyed in this study. Each driver is considered as a DMU and his/her efficiency is measured using the preferred model of DEA. Based on the injury severity and styles of decision-making, the data set is clustered into two categories. The first category (related to injury severity) contains three groups including without injury (ISS  25), minor injury (25 < ISS < 55), and severe injury (ISS  55) with 194, 169 and 165 DMUs,

Conclusions

One of the major threats of public health is road accidents that can be caused by human and decision factors of drivers. This study for the first time, investigated drivers’ decision-making styles versus severity of accidents by using a novel framework in which two standard questionnaires, robust mathematical models and design of experiments were employed. Two questionnaires were distributed randomly among 528 drivers in Tehran. The first questionnaire includes 33 items about drivers’

Acknowledgements

The authors are grateful for the valuable comments and suggestions from the respected reviewers. Their valuable comments and suggestions have enhanced the strength and significance of our paper. This study was supported by a grant from University of Tehran (Grant No. 8106013/1/20). The authors are grateful for the support provided by the College of Engineering, University of Tehran, Iran. This study was also supported by a grant from the Iran National Science Foundation (Grant No. 94002128).

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