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Step 7 in the scalable risk assessment process consists of compiling other data besides exposure that is required based upon the risk definition selected in Step 3. The three possible risk definitions are:
Detailed instructions for compiling other required data for these three risk definitions is beyond the scope of this guide. There is extensive guidance and examples in several other reports, manuals, and guides. Therefore, the following sections provide summary information and pointers to these other guidance documents.
To calculate observed crash rate, reported pedestrian and bicyclist crash data are compiled from existing state and local crash databases. The exact procedures for obtaining and compiling crash data vary from state to state (as well as the crash data attributes). Therefore, this section provides an overview and points to other published resources and guide. In particular, these FHWA documents are relevant for compiling crash data:
Each agency that provides crash data will typically provide documentation and data dictionaries that describe crash database attributes. Typically, a crash database contains three major components:
If observed crashes are being used to quantify risk, the possibility of unreported crashes should be considered as a potential bias. In some cases, safety analysts will supplement official crash databases with other sources of data, such as that from emergency medical services, hospital outcomes, and public health databases. Considering these other sources may help to provide a more comprehensive database of pedestrian and bicyclist crashes.
Expected pedestrian and bicyclist crashes can be estimated by:
In either case, exposure is considered an important factor in estimating expected crashes. Therefore, the exposure values developed in Step 6 will be used in this step to estimate expected crashes.
Safety analysts estimate expected crashes to overcome several issues associated with observed crashes. Observed crashes (especially pedestrians and bicyclists) can be a rare occurrence, and the actual observed number of crashes at specific locations may not accurately represent the risk to pedestrians and bicyclists.
The HSM has developed safety performance functions that are used to calculate predicted crashes. Then, Empirical Bayes procedures are used to estimate expected crashes (which is a weighted average of observed crashes and predicted crashes). However, at the time of this writing, the HSM procedures for pedestrian and bicyclist crashes are still being refined and are not comprehensive (e.g., they do not address crashes on rural roads). NCHRP Project 17-84 was initiated in early 2017 to improve guidance for pedestrian and bicyclist crash prediction in future editions of the HSM.
Several efforts have developed crash prediction models aside from those in the HSM. The development of crash prediction models is outside the scope of this Guide, but the following list includes examples of crash model development for interested readers:
In this definition of risk, analysts develop and compile additional risk indicators that have been defined in Step 3. The actual risk indicators may vary depending upon the location and facilities being analyzed, and are identified as part of a systemic safety evaluation process (or similar process). FHWA provides several resources for systemic safety at https://safety.fhwa.dot.gov/systemic/. In particular, three documents are relevant:
The case studies in Report FHWA-SA-17-002 include an example of systemic analysis for pedestrian and bicyclist crashes, and this example was included earlier in this guide.
Attributes from roadway inventory and traffic count databases are often the starting point for identifying risk factors. For example, FHWA recommends the following list of potential risk factors for consideration in systemic safety analyses.
Roadway and Intersection Features
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