Multivariate Poisson-Lognormal Regression for Crash Prediction of Different Types on Freeways Diverge Areas

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The primary objective of this study was to develop a multivariate Poisson-lognormal regression model (MVPLN) to evaluate the effects of contributing factors to crash frequency and to identify the correlations among different types. Crash data were collected at 282 exit-ramps on freeways in Florida, US.The Markov chain Monte Carlo (MCMC) method was used to get the solutions of the MVPLN model.The effects of crash-related factors of different types were explored in the MVPLN model considering the correlation structure.It was found thatmainline lane number was the contributing factor for both single-lane and two-lane exit ramp.

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683-688

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November 2012

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