Influence of segmentation approaches on the before-after evaluation of engineering treatments: A hypothetical treatment approach.

The segmentation of highways is a fundamental step in estimating crash frequency models and conducting a before-after evaluation of engineering treatments, but the effects of segmentation approaches on the engineering treatment evaluations are not known very well. This study examined the effects of segmentation approaches on the before-after evaluation of engineering treatments. In particular, this study evaluated four segmentation approaches by applying the Empirical Bayes technique to a dataset for which the ground truth was known. Four segmentation approaches included Highway Safety Manual (HSM), Fixed (kilometre post), Fisher's, and K-means segmentation. This study utilized a 440 km stretch of rural two-lane two-way highway in Queensland, Australia, to prepare a dataset with known ground truth. The treatment under evaluation was a hypothetical treatment, which should yield a crash modification factor (CMF) of 1. For assigning hypothetical treatment, a total of fifteen datasets were prepared, including ten datasets based on the random assignment and five datasets based on the hotspot identification method. Following the before-after evaluation using the Empirical Bayes technique, the results showed that HSM and Fixed segmentation approaches predict the ground truth in both dataset types. From random assignment datasets, the estimated CMFs using HSM, Fixed, Fisher's, and K-means segmentation approaches deviated from the true CMF (i.e., 1) by 2.32 %, 5.30 %, 6.08 %, and 8.62 %, respectively. In the case of hotspots, the corresponding deviations of CMFs were 8.57 %, 9.37 %, 28.84 %, and 35.43 %, respectively. Overall, HSM segmentation best identified the actual treatment effect, followed by the Fixed segmentation. If the variables to define homogeneity for HSM segmentation are limited, then Fixed segmentation can yield reliable crash modification factors from the before-after treatment evaluations than the crash-based segmentation approaches.