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Suggested Citation:"Summary." National Academies of Sciences, Engineering, and Medicine. 2019. Development of Roundabout Crash Prediction Models and Methods. Washington, DC: The National Academies Press. doi: 10.17226/25360.
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1 S U M M A R Y This report presents the approach and findings of NCHRP Project 17-70, “Development of Roundabout Crash Prediction Models and Methods.” Kittelson & Associates, Inc., working with Persaud & Lyon, Inc., and Write Rhetoric, collected extensive crash, volume, and geometric data for over 350 roundabouts in the United States. From this database, they developed crash prediction models for urban/suburban and rural single-lane and multilane roundabouts. The crash prediction models are organized into three basic types: 1. Planning-level crash prediction models, 2. Intersection-level crash prediction models, and 3. Leg-level crash prediction models. The planning-level models are relatively easy to apply (fewer data needs) and are intended to be used early in the project development process in activities such as network screening or intersection control evaluations where the potential safety performance of one type of control is being compared to another (e.g., potential safety performance of a signal versus a roundabout at a specific intersection). The intersection-level crash prediction models were developed to inform design decisions at a similar level of detail as to the intersection crash prediction models for signals and two-way stop-controlled intersections in the Highway Safety Manual, 1st edition. The leg-level crash prediction models were developed to inform design decisions at the leg level and serve as a comparison to models from abroad. Through the international literature review conducted (and documented in Chapter 2), it was clear that leg-level crash prediction models for roundabouts are frequently developed. This project also considered the potential effects of driver learning curve on crash fre- quency and severity at roundabouts. With the data available, it was not possible to quantify or identify with certainty that driver learning curve influences crash frequency and severity. For crash severity, the KABCO injury severity scale was used. Where, K indicates a fatal crash (a person died within 30 days of being involved in the crash), A is a severe injury crash, B is a moderate injury crash, C is a minor injury crash, and O is a property damage–only crash. A crash is categorized based on the most severe injury sustained. This report is organized such that Chapter 1 provides the background for the project. Chapters 2 through 5 document the literature review, data collection, and modeling frame- work and detail the modeling approach. Chapter 6 presents the findings from the research. Chapter 7 provides the conclusion. In addition, this project provided draft text to update the Highway Safety Manual to include the crash prediction models described and documented in this report. Development of Roundabout Crash Prediction Models and Methods

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TRB’s National Cooperative Highway Research Program (NCHRP) Research Report 888: Development of Roundabout Crash Prediction Models and Methods provides crash prediction models that quantify the expected safety performance of roundabouts for motorized and non-motorized road users. Safety performance factors (SPF) and crash modification factors (CMF) are predictive models that estimate expected crash frequencies. These models are used to identify locations where crash rates are higher than expected, to estimate safety benefits of a proposed project, and to compare the safety benefits of design alternatives. SPF and CMF models may help identify and prioritize locations for safety improvements, compare project alternatives by their expected safety benefits, and guide detailed design decisions to optimize safety. Research indicates that roundabouts provide substantial reductions in crashes, and this report determines SPF and CMF specifications for roundabouts.

The report includes appendices to the contractor's final report and a Powerpoint presentation.

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