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Description:
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Statistical models have been very popular for estimating the performance of highway
safety improvement programs which are intended to reduce motor vehicle crashes . The
traditional Poisson and Poisson -gamma (negative binomial ) models are the most popular
probabilistic models used by transportation safety analysts for analyzing traffic crash
data . The Poisson -gamma model is usually preferred over traditional Poisson model
since crash data usually exhibit over -dispersion . Although the Poisson -gamma model is
popular in traffic safety analysis , this model has limitations particularly when crash data
are characterized by small sample size and low sample mean values . Also , researchers
have found that the Poisson -gamma model has difficulties in handling under -dispersed
crash data . The primary objective of this research is to evaluate the performance of the
Conway -Maxwell -Poisson (COM -Poisson ) model for various situations and to examine
its application for analyzing traffic crash datasets exhibiting over - and under -dispersion .
This study makes use of various simulated and observed crash datasets for accomplishing
the objectives of this research .
Using a simulation study , it was found that the COM -Poisson model can handle under - ,
equi - and over -dispersed datasets with different mean values , although the credible
intervals are found to be wider for low sample mean values . The computational burden of
its implementation is also not prohibitive . Using intersection crash data collected in
Toronto and segment crash data collected in Texas , the results show that COM -Poisson
models perform as well as Poisson -gamma models in terms of goodness -of -fit statistics and predictive performance . With the use of crash data collected at railway -highway
crossings in South Korea , several COM -Poisson models were estimated and it was found
that the COM -Poisson model can handle crash data when the modeling output shows
signs of under -dispersion . The results also show that the COM -Poisson model provides
better statistical performance than the gamma probability and traditional Poisson models .
Furthermore , it was found that the COM -Poisson model has limitations similar to that of
the Poisson -gamma model when handling data with low sample mean and small sample
size . Despite its limitations for low sample mean values for over -dispersed datasets , the
COM -Poisson is still a flexible method for analyzing crash data . |