01698341

Component

Crash modification factors (CMFs) play important roles in roadway safety management. Safety analysts have proposed various methods for developing CMFs, and nearly all of them are crash count-based. Intervals between crashes are not considered, while survival theory has been widely used in other fields. The objective of this study is to incorporate survival models into the estimation of CMFs and to examine if it increases estimation accuracy. To accomplish the objective, this study proposed a joint modeling approach, simultaneously analyzing crash counts and time intervals between crashes, for estimating CMFs. In order to assess the performance, this study developed CMFs for a dummy treatment at 90 sites on rural two-lane highways in Texas, with the standard empirical Bayes (EB) method and the joint model, separately. The findings are very interesting: (1) The standard EB method tends to over-estimate the CMFs for the treatment, and under-estimate the standard error of the CMFs. Most of the cases, the results are biased; (2) The CMFs developed with the joint model have greater standard errors, but their values are closer to the true effects, which is more realistic; (3) Temporary instability in traffic crashes are also observed in this study. Increasing the duration of study period does not always increase the accuracy of CMF estimates. Roadway agencies are encouraged to deploy the joint model for dynamical monitoring safety effects of treatments by flexible feedback.

This paper was sponsored by TRB committee ANB25 Standing Committee on Highway Safety Performance.

19-03454

English

6p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

References (20)

Bayes' theorem; Crash characteristics; Crash modification factors; Crash risk forecasting; Estimating; Highway safety; Mathematical models

Data and Information Technology; Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-03454

TRIS, TRB, ATRI

Dec 7 2018 9:52AM