• Journal of the Korean Society of Safety
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• v.27 no.4
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• pp.108-114
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• 2012

Traffic Accident Merging Index(TAMI) is developed for TMACS(Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. This paper suggest the accurate TAMI prediction model by time series forecasting. Preventing the traffic accident by accurately predicting it in advance can greatly improve road traffic safety. Searches the model which minimizes the error of 230 local self-governing groups. TAMI of 2007~2009 years data predicts TAMI of 2010. And TAMI of 2010 compares an actual index and a prediction index. And the error is minimized the constant where selects. Exponential Smoothing model was selected. And smoothing constant was decided with 0.59. TAMI Forecasting model provides traffic next year safety information of the local government.

• Journal of the Korean Society of Safety
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• v.27 no.3
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• pp.147-152
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• 2012

Traffic Accident Merging Index (TAMI) is developed for TMACS (Traffic Safety Information Management Complex System). TAMI is calculated by combining 'Severity Index' and 'Frequency'. The existing indexes are Traffic deaths per 100,000 population, Traffic deaths per 100,000 inhabitants/per billion veh-km, etc. However, there is no consistency in using them among local governments, so it can create confusion. Moreover, the index level is too complicated to understand. Therefore, this study suggests new traffic safety index, TAMI. It will work to improve the weaknesses and present accurate status of traffic safety in local governments.