Examining the application of conway-maxwell-poisson models for analyzing traffic crash data

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Title: Examining the application of conway-maxwell-poisson models for analyzing traffic crash data
Author: Geedipally, Srinivas Reddy
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2333
Date: 2009-05-15

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Examining the application of conway-maxwell-poisson models for analyzing traffic crash data. Available electronically from http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2333 .

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