Modeling Traffic Accident by Taxi Drivers through Overdispersion Test
Tae-Youn Jang · 2003
This study has the research purpose for establishing model considering overdispersion appeared frequently in count data and applies the model into traffic accidents by taxi drivers. The model usually used in current research on count data is based on linear regression analysis because of its simple concept and easy usage. But linear regression model has the following shortcoming; unreflection of non-negative integer characteristics in count data, unreliability of forecasting results, and not offering of discrete probability on traffic accident frequencies. The model appropriate for analyzing count data is poisson regression model. However, poission regression model is based on the concept that expected mean of distribution is equal to variance. The most count data has variance greater than mean, which results in underestimating standard error. In study, overdispersion test is used to estimate the degree of statistical difference between mean and variance. The result of test proves that negative binomial regression model rather than poisson regression model well reflects traffic accident frequencies of taxi drivers in this study. Likelihood ratio test and Theil's inequality coefficient test are applied for the validity of variables and for accuracy of estimating model.