In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released the first edition of the Highway Safety Manual (HSM). The HSM introduces a six-step safety management process which provides engineers with a systematic and scientific approach to managing road safety. The first step of this process, network screening, aims to identify the locations that will most benefit from a safety improvement program. The output obtained from network screening is simply a list of locations that have a high concentration of collisions, based on their potential for safety improvement. The ranking naturally tends to lead to the assumption that the most highly ranked locations are the obvious target locations where road authorities should allocate their often-limited road safety resources. Though these locations contain the highest frequency of collisions, they are often spatially unrelated, and scattered throughout the roadway network. Allocating safety resources to these locations may not be the most effective method of increasing road safety.
The purpose of this research is to investigate and validate a two-step method of post-network screening analysis, which identifies collision hotzones (i.e., groups of neighboring hotspots) on a road network. The first step is the network screening process described in the HSM. The second step is new and involves network-constrained kernel density estimation (KDE), a type of spatial analysis. KDE uses expected collision counts to estimate collision density, and outputs a graphical display that shows areas (referred to here as hotzones) with high collision densities. A particularly interesting area of application is the identification of high-collision corridors that may benefit from a program of systemic safety improvements. The proposed method was tested using five years of collision data (2005-2009) for the City of Regina, Saskatchewan. Three different network screening measures were compared: 1) observed collision counts, 2) observed severity-weighted collision counts, and 3) expected severity-weighted collision counts. The study found that observed severity-weighted collision counts produced a dramatic picture of the City's hotzones, but this picture could be misleading as it could be heavily influenced by a small number of severe collisions. The results obtained from the expected severity-weighted collision counts smoothed the effects of the severity-weighting and successfully reduced regression-to-the-mean bias. A comparison was made between the proposed approach and the results of the HSM’s existing network screening method. As the proposed approach takes the spatial association of roadway segments into account, and is not limited to single roadway segments, the identified hotzones capture a higher number of expected EPDO collisions than the existing HSM methodology. The study concludes that the proposed two-step method can help transportation safety professionals to prioritize hotzones within high-collision corridors more efficiently and scientifically.
Jurisdiction-specific safety performance functions (SPFs) were also developed over the course of this research, for both intersections (three-leg unsignalized, four-leg unsignalized, three and four-leg signalized), and roadway segments (major arterials, minor arterials, and collectors). These SPFs were compared to the base SPFs provided in the HSM, as well as calibrated HSM SPFs. To compare the different SPFs and find the best-fitting SPFs for the study region, the study used statistical goodness-of-fit (GOF) tests and cumulative residual (CURE) plots. Based on the results of this research, the jurisdiction-specific SPFs were found to provide the best fit to the data, and would be the best SPFs for predicting collisions at intersections and roadway segments in the City of Regina
