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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Willingness to pay for mortality risk reduction for traffic accidents in Myanmar
Ei Ei Mon1, Sajjakaj Jomnonkwao1, Buratin Khampirat2
1School of Transportation Engineering, Institute of Engineering, Suranaree University of Technology, 111 University Avenue, Suranaree Sub-district, Muang District, NakhonRatchasima, 30000, Thailand.
Abstract:
The dramatic increase in vehicle ownership in Myanmar over the past few years has resulted in an alarming increase in traffic accidents. Thus, road safety at the national level needs to be improved urgently in order to reduce the costs associated with traffic accidents and to assist policy makers in making economically efficient resource allocation decisions for road safety improvements. This research was conducted to determine the costs related to fatality risk reductions using a willingness to pay (WTP) approach for motorcyclists, car drivers, and bus passengers in Myanmar. Face-to-face interviews with contingent valuation (CV) and a payment card questionnaire approach was employed for the data collection; multiple linear regression analyses were conducted to determine the factors influencing WTP. The resulting median and mean for the value of statistical life (VSL) were found to be MMK 118.062 million (US$ 98,385) to MMK 162.854 million (US$ 135,712), respectively. Therefore, the total cost of death was estimated to range from MMK 594.681 billion (US$ 495.567 million) to MMK 820.296 billion (US$ 683.580 million) in 2015. In addition, the WTP was found to be significantly associated with age, family status, education, occupation, individual income, household income, the vehicle used, exposure to traffic, drunk driving, personal experiences, and the perceived risk of traffic accidents. This study might be helpful in prioritization of road safety related projects to get greatest benefit by choosing most cost effective projects. This study might assist the decision-making for road safety budget allocations and policy development.
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