Crash Frequency Analysis with Generalized Additive Models Xie, Yuanchang ; Zhang, Yunlong
Series: ; 2061Publication details: Transportation Research Record: Journal of the Transportation Research Board, 2008Description: s. 39-45ISBN:- 9780309113311
Current library | Status | |
---|---|---|
Statens väg- och transportforskningsinstitut | Available |
Recent crash frequency studies have been based primarily on generalized linear models, in which a linear relationship is usually assumed between the logarithm of expected crash frequency and other explanatory variables. For some explanatory variables, such a linear assumption may be invalid. It is therefore worthwhile to investigate other forms of relationships. This paper introduces generalized additive models to model crash frequency. Generalized additive models use smooth functions of each explanatory variable and are very flexible in modeling nonlinear relationships. On the basis of an intersection crash frequency data set collected in Toronto, Canada, a negative binomial generalized additive model is compared with two negative binomial generalized linear models. The comparison results show that the negative binomial generalized additive model performs best for both the Akaike information criterion and the fitting and predicting performance.