States are required (23 U.S.C. 148) to perform safety project identification and analysis as part of the HSIP. However, the law does not specify the methodologies states shall use. The HSIP Manual (FHWA-SA-09-029) outlines the following steps for project identification: collect and analyze data; identify crash types and contributing factors; establish a crash pattern; conduct field reviews; identify countermeasures; assess countermeasure effectiveness; and use the current science (e.g., crash modification factors) to determine and prioritize project selection. The goal is to use data-driven decision making to identify and prioritize projects with the greatest potential for reducing deaths and serious injuries on all public roadways.

In practice, methods used to identify candidate project locations vary significantly from state to state. Many states identify potential locations for safety improvements based on crash frequency or rate, while some have begun to use more advanced methods that incorporate safety performance functions (SPFs) or the Empirical Bayes (EB) method. In addition, some states are changing focus from “hot spot” improvements to a systemic approach. Qualitative information commonly used to identify candidate safety projects include panel reviews, input from public and law enforcement, field reviews, and road safety audits (RSA).

Some state departments of transportation (DOT) select projects at the state level while others distribute funds to DOT District offices to use at each district’s discretion. Many states selecting projects at the state level solicit projects from DOT District offices and local agencies for consideration. States commonly conduct benefit-cost analyses to select and prioritize projects and rank them first using the highest benefit-cost ratio or net present value.

One of the biggest challenges to effective project identification is the lack of data, particularly for local roadways. Even when quality data are available, many states do not have the training, resources, or tools to apply the more advanced and rigorous data analysis methods necessary to use them effectively. In addition, competing political or institutional realities could impose non-data driven factors on the decision-making process, making it difficult to select those HSIP projects with the greatest potential to improve safety.

While many considerations enter into project selection, quantitative analysis should be used whenever possible in the prioritization process (e.g., comparing cost, effectiveness, and lifespan of the project). Quantitative information lends objectivity to the decision making process and helps maximize the safety benefit for the resources invested.

Noteworthy Practices

The following cases demonstrate noteworthy practices several states are using in HSIP project identification:

• The North Carolina DOT (NCDOT) developed four categories of safety warrants used in the network screening process to identify locations with severe crashes and crash patterns that can be addressed by engineering safety countermeasures. To provide a clear and consistent data-driven process, NCDOT developed a decision support tool to perform the initial prioritization of all candidate safety projects from across the state. (read more)
• The Missouri DOT (MoDOT) made the state’s HSIP more proactive through the systemwide implementation of engineering strategies described in Missouri’s Blueprint to Arrive Alive (Strategic Highway Safety Plan). Using HSIP funds, MoDOT incorporates the installation of rumble strips/stripes, improved signing and delineation, wider pavement markings, and improved shoulders into pavement resurfacing projects. Since 2007, almost two-thirds of MoDOT’s HSIP funds have been allocated to systemic improvements, resulting in a safer system overall. (read more)
• The Minnesota DOT (MnDOT) restructured its HSIP to provide funding for local agencies to address the large proportion of severe crashes occurring on local roadways, and developed funding goals for proactive and reactive improvements. MnDOT developed a “proactive spectrum” to establish safety funding goals for the Metropolitan District (Minneapolis/St. Paul area) and rural districts. Minnesota has successfully increased the proportion of safety funding spent on proactive improvements. Almost 90 percent of projects programmed for fiscal year 2010-2011 are proactive. (read more)
• The Illinois DOT (IDOT), with the assistance of the University of Illinois, developed safety performance functions (SPFs) for all state routes and intersections using the Empirical Bayes (EB) method. IDOT uses the SPFs in the network screening process to identify locations with the highest potential for safety improvement. The use of SPFs in the network screening process enables the state to shift emphasis of the HSIP away from focusing on urban densely populated areas. The resulting broader focus includes low-cost safety improvements or systemic improvements that may not have been identified using previous screening methods. (read more)
• The Colorado DOT (CDOT) developed sophisticated predictive and diagnostic tools that incorporate calibrated SPFs for all public roadway types and intersections in the state. These tools enable CDOT to maximize potential crash reduction in the state within the constraints of available budgets. CDOT institutionalized the use of these tools by applying them to all CDOT projects. Over the seven years of applying these methods on all infrastructure projects, the state has achieved an unprecedented fatal crash reduction of 36 percent. (read more)

To access these full case studies, click on the individual links above or visit the FHWA Office of Safety on-line at: http://www.fhwa.dot.gov/safety/legislative-safety-programs/hsip.