A New Holistic Crashes Prediction Model Based on Zero-Truncated Data For Intercity Four-Lane Highways Curves

This study aimed at exploring the effect of consistent design and self-explanatory the horizontal curves on crash frequency in four-lane highways using zero-truncated crashes data. the present study has considered the related variables for 45 curves of four-lane intercity highways in the three-year (2018-2020). the standard poisson distribution is a benchmark for modeling equi-dispersed count data and could not express under-dispersed zero-truncated data. modeling was performed using poisson, negative binomial, zero-truncated poisson, zero-truncated negative binomial, and conway-maxwell poisson (com-poisson) regression. the results revealed that the com-poisson regression distribution could effectively fit the model under-dispersed zero-truncated crashes data. according to the results, the ratio of the curve radius to the average radius of the adjacent curves (as the alignment consistency variable), the difference between the expected and the existing superelevation (as the vehicle stability consistency variable), and the variables of the total self-explaining index and heavy vehicle percentage have significant relationships with crash frequency. as a result, increasing the self-explanatory features of curves is an efficient and prioritized policy for safety improvement on the intercity four-lane highways.