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ROAD ACCIDENT COST PREDICTION MODEL USING SYSTEMS DYNAMICS APPROACH

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Accident costs are an important component of external costs of traffic, a substantial part is related to fatal accidents. The evaluation of fatal accident costs crucially depends on the availability of an estimate for the economic value of a statistical life. This paper aims to develop a model for road accident through systems dynamics approach. To build an accident model, various factors causing the road accident and cost were identified. This model is capable of calculating the accident rate and its costs for the future. In this study the accident caused by bus alone is considered. The cost model is dealt more in this study as it requires more complex assessment. The accident model is built on the year 2000 data and predicted the accidents up to 2020 for every 5‐year interval. The accident model is valuated by comparing the predicted and actual accident data for the year 2005. Three scenarios were studied by changing the income growth rate and discount rate. Finally, best scenario is suggested for implementation. The outcome of the study is highly useful for the planners, administrators and police to make their decisions effectively for road safety investment projects.
Title: ROAD ACCIDENT COST PREDICTION MODEL USING SYSTEMS DYNAMICS APPROACH
Description:
Accident costs are an important component of external costs of traffic, a substantial part is related to fatal accidents.
The evaluation of fatal accident costs crucially depends on the availability of an estimate for the economic value of a statistical life.
This paper aims to develop a model for road accident through systems dynamics approach.
To build an accident model, various factors causing the road accident and cost were identified.
This model is capable of calculating the accident rate and its costs for the future.
In this study the accident caused by bus alone is considered.
The cost model is dealt more in this study as it requires more complex assessment.
The accident model is built on the year 2000 data and predicted the accidents up to 2020 for every 5‐year interval.
The accident model is valuated by comparing the predicted and actual accident data for the year 2005.
Three scenarios were studied by changing the income growth rate and discount rate.
Finally, best scenario is suggested for implementation.
The outcome of the study is highly useful for the planners, administrators and police to make their decisions effectively for road safety investment projects.

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