Safety Analysis Workflows
Network Screening
- SSPF (Safer Streets Priority Finder)
- SSPF is a web based tool that enables users to perform analysis used in network screening to identify streets with elevated safety risk.
- SSPF tool has a dashboard that can be used for descriptive safety analysis (DSA). DSA helps in exploring trends and patterns in the data by slicing and dicing it different ways. DSA is often a first step in safety analysis prior to performing other analysis such as High Injury Network (HIN) and systemic safey analysis.
- SSPF website has helpful documentation and links to instructional material that is useful in using the tool. The America Walks webinar session on using SSPF is another resource to assist users in making use of SSPF.
- geopandas & linref (Python Libraries)
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Python libraries such as geopandas and linref (see Applications and Tools page for more details) can be used to perform network screening analysis by ocmbinng crash data with roadways and othercontextual data.
While these methods are powerful and enable highly customizable apporaches, implementing them will require some programming knowledge.
- GeoPandas Documentation
- linref Documentation
- Other scripted methods
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In addition to the Python modules mentioend above, several alternative scripted options are available to do network screening.
R and PostGIS (see Applications and Tools page) are just two such alternative sources that allow for a dynamic and customizable analysis.
These programming languages and tools can also be used together to leverage the strengths of each of them in a very effctive way.
SSPF is an example of a tool was built by combining multiple workflows (R, PostGIS, Python, command line tools, etc).
- QGIS or other GIS Applications
- QGIS and most other GIS applications have built-in geoprocessing tools that enables users to consolidate crash, roadway, and contextual data in a way that is useful for network screening. These GIS applications usually have a low barrier to entry in terms of techincal knowledge, but their customizabilty and being able to dynamically perform large scale analysis might be more limited than using scripted methods mentioned above.
Crash Prediction
The Highway Safety Manual’s (HSM) crash prediction modeling approach is a data-driven method used by transportation agencies to estimate expected crash frequencies for various roadway types and conditions. It supports safety decision-making by evaluating roadway design for safety performance, identifying the greatest opportunities for safety improvement on existing roads, and evaluating the potential effectiveness of safety countermeasures.
- AASHTO Highway Safety Manual (HSM) Spreadsheets
- AASHTO offers a set of free crash prediction spreadsheets tools to help practitioners apply the advanced predictive methods outlined in Part C of the Highway Safety Manual. The spreadsheets are designed to be simple and intuitive and have been used extensively among state and local agencies.
- IHSDM (Interactive Highway Safety Design Model)
- The Interactive Highway Safety Design Model (IHSDM) is a free FHWA software tool that helps public agencies apply the crash prediction methods in Part C of the Highway Safety Manual. Its Crash Prediction Module enables users to estimate crashes, evaluate safety impacts of roadway design alternatives, and assess the cost-effectiveness of improvements, with additional modules supporting design consistency, intersection review, and more. Note that this tool is being sunset by FHWA though it is still available for use.
- SPF-R (Safety Performance Function Resources)
- SPF-R is a web-based tool and coding library developed by the University of Kentucky to support transportation agencies in developing and applying Safety Performance Functions (SPFs) for predictive network screening analysis. For the web-based tool, users upload data for analysis and follow steps outlined in a comprehensive user guide. Sample datasets are also available for exploring and testing the tool.
- CPM.py
- CPM.py is an ongoing open-source project aimed at enabling crash prediction modeling using Highway Safety Manual (HSM) Part C models through a simple, Python-based workflow. Existing pilot tools allow for easy analysis using several models present in the HSM first edition and will be expanded in the future to include models added in the second edition.
Countermeasure Effectiveness
- CMF Clearinghouse
- Crash Modification Factors (CMF) Clearinghouse is an online resource developed by FHWA to provide research-based metrics on the effectiveness of roadway safety countermeasures. It allows transportation professionals to search, compare, and study CMFs to estimate the expected crash reduction benefits of specific treatments and inform data-driven safety decisions.
Data-Driven Prioritization
- Prioritization Logic and Scoring
- (Custom methodologies; no centralized public resource link)
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