@article{ref1,
title="Incorporating survival analysis into the safety effectiveness evaluation of treatments: jointly modeling crash counts and time intervals between crashes",
journal="Journal of transportation safety and security",
year="2022",
author="Wu, Lingtao and Meng, Yi and Kong, Xiaoqiang and Zou, Yajie",
volume="14",
number="2",
pages="338-358",
abstract="This study aims to incorporate survival theory into the estimation of crash modification factors (CMFs) and to examine the performance. A joint modeling framework, which considers crash counts and time intervals between crashes simultaneously, is proposed. To assess the performance of the joint model, this study collected roadway and crash data on 240 rural two-lane roadway segments in Texas, developed CMFs for a dummy treatment and further compared the results with those developed using the traditional empirical Bayes (EB) method. The main findings are summarized as follows: (1) The traditional EB method tends to overestimate the CMFs for the treatment, and underestimate the standard errors. In most cases, the results are biased; (2) The estimated CMF values with the joint model are closer to the true effect, and they have higher standard errors. The confidence intervals of the CMFs cover the CMF for the dummy treatment (i.e., 1.0), which is more realistic; (3) Temporal instability in traffic crashes are also observed in this study. Increasing the duration of the study period does not always increase the accuracy of CMF estimates. In addition to crash counts, safety analysts are encouraged to incorporate time intervals between crashes while estimating CMFs.<p /> <p>Language: en</p>",
language="en",
issn="1943-9962",
doi="10.1080/19439962.2020.1786871",
url="http://dx.doi.org/10.1080/19439962.2020.1786871"
}