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Performance-Based Management of Traffic Signals (2020)

Chapter: Chapter 3 - Performance Measure Details

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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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Suggested Citation:"Chapter 3 - Performance Measure Details." National Academies of Sciences, Engineering, and Medicine. 2020. Performance-Based Management of Traffic Signals. Washington, DC: The National Academies Press. doi: 10.17226/25875.
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CHAPTER 3 PERFORMANCE MEASURE DETAILS 3.1 COMMUNICATION STATUS 36 3.2 FLASH STATUS 38 3.3 POWER FAILURES 40 3.4 DETECTION SYSTEM STATUS 42 3.5 VEHICLE VOLUMES 44 3.6 PHASE TERMINATION 48 3.7 SPLIT MONITOR 51 3.8 SPLIT FAILURES 53 3.9 ESTIMATED VEHICLE DELAY 58 3.10 ESTIMATED QUEUE LENGTH 62 3.11 OVERSATURATION SEVERITY INDEX 65 3.12 PEDESTRIAN VOLUMES 68 3.13 PEDESTRIAN PHASE ACTUATION AND SERVICE 70 3.14 ESTIMATED PEDESTRIAN DELAY 74 3.15 ESTIMATED PEDESTRIAN CONFLICTS 77 3.16 YELLOW/RED ACTUATIONS 79 3.17 RED-LIGHT-RUNNING (RLR) OCCURRENCES 82 3.18 EFFECTIVE CYCLE LENGTH 85 3.19 PROGRESSION QUALITY 88 3.20 PURDUE COORDINATION DIAGRAM 92 3.21 CYCLIC FLOW PROFILE 96 3.22 OFFSET ADJUSTMENT DIAGRAM 100 3.23 TRAVEL TIME AND AVERAGE SPEED 104 3.24 TIME-SPACE DIAGRAM 108 3.25 PREEMPTION DETAILS 110 3.26 PRIORITY DETAILS 113 3.27 REFERENCES 116

LIST OF EXHIBITS EXHIBIT 3-1. SIGNAL PERFORMANCE MEASURE DESCRIPTIONS 32 EXHIBIT 3-2. SIGNAL PERFORMANCE MEASURE INPUTS AND OUTPUTS 34 EXHIBIT 3-3. COMMUNICATION SYSTEM STATUS EXAMPLE: NUMBER OF SIGNALIZED INTERSECTIONS BY CORRIDOR OFFLINE OVER 1 MONTH (DAY, BULLOCK ET AL. 2016) 37 EXHIBIT 3-4. FLASH STATUS EXAMPLE: NUMBER OF FLASH EVENTS PER INTERSECTION BY HOUR OF THE DAY OVER 2 MONTHS (COURTESY PURDUE UNIVERSITY) 39 EXHIBIT 3-5. POWER FAILURES EXAMPLE: NUMBER OF POWER FAILURE EVENTS BY CORRIDOR OVER 6 MONTHS (COURTESY PURDUE UNIVERSITY) 41 EXHIBIT 3-6. DETECTION SYSTEM STATUS EXAMPLE: NUMBER OF SIDE-STREET PHASES SERVED EVERY CYCLE LATE AT NIGHT (INDICATING FAILED DETECTORS) PER CORRIDOR OVER 4 MONTHS (DAY, BULLOCK ET AL. 2016) 43 EXHIBIT 3-7. VOLUMES EXAMPLE: VOLUME PROFILES AND PLANNING METRICS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 46 EXHIBIT 3-8. VOLUMES EXAMPLE: VOLUME PROFILES DURING A SPECIAL EVENT (COURTESY OREGON DEPARTMENT OF TRANSPORTATION) 47 EXHIBIT 3-9. PHASE TERMINATION EXAMPLE: DISTRIBUTION OF TERMINATION TYPES BY PHASE (DAY ET AL. 2014) 50 EXHIBIT 3-10. SPLIT MONITOR EXAMPLE: DETOUR ROUTE 52 EXHIBIT 3-11. SPLIT MONITOR EXAMPLE: NORTHBOUND LEFT-TURN PHASE AFFECTED BY I-15 CLOSURE (MACKEY 2017) 52 EXHIBIT 3-12. VOLUME-TO-CAPACITY V/C RATIO CALCULATION 54 EXHIBIT 3-13. SPLIT FAILURES EXAMPLE: NUMBER OF SPLIT FAILURES PER HOUR FOR SEVEN CORRIDORS (LI ET AL. 2017) 56 EXHIBIT 3-14. SPLIT FAILURES EXAMPLE: NUMBER OF SPLIT FAILURES BEFORE AND AFTER IMPLEMENTATION OF AN ADAPTIVE CYCLE LENGTH ALGORITHM (RICHARDSON ET AL. 2017) 57 EXHIBIT 3-15. MAXIMUM VEHICLE DELAY AND TIME TO SERVICE CALCULATIONS 59 EXHIBIT 3-16. ESTIMATED DELAY EXAMPLE: CUMULATIVE DISTRIBUTIONS OF MAXIMUM VEHICLE DELAY FOR EIGHT PHASES BEFORE AND AFTER SPLIT ADJUSTMENT (LAVRENZ ET AL. 2015) 61 EXHIBIT 3-17. ESTIMATED QUEUE LENGTH EXAMPLE: CHART OF QUEUE LENGTHS ON A SIGNALIZED APPROACH IN INDIANA (DAY, BULLOCK ET AL. 2014) USING METHOD PRESENTED BY LIU AND MA (2009) 64 EXHIBIT 3-18. OVERSATURATION SEVERITY INDEX EXAMPLE: (A) SPATIAL (SOSI) AND (B) TEMPORAL (TOSI) INDICES FROM A SIMULATION ENVIRONMENT (GETTMAN, MADRIGAL ET AL. 2012) 67 EXHIBIT 3-19. PEDESTRIAN VOLUMES EXAMPLE: PEDESTRIAN COUNT DATA FROM A TRAIL LOCATION (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 69 EXHIBIT 3-20. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: PEDESTRIAN PUSH BUTTON ACTUATIONS RELATIVE TO WALK TIMES (DAY, TAYLOR ET AL. 2016) 71 EXHIBIT 3-21. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: PERCENTAGE OF CYCLES WITH PEDESTRIAN PHASES BEFORE AND AFTER IMPLEMENTATION OF AN EXCLUSIVE PEDESTRIAN PHASE (DAY, PREMACHANDRA, AND BULLOCK 2011) 72 EXHIBIT 3-22. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: NUMBER OF PEDESTRIAN CALLS PER DAY BY INTERSECTION IN THE SALT LAKE CITY AREA (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 73 EXHIBIT 3-23. PEDESTRIAN DELAY EXAMPLE: PEDESTRIAN DELAY FOR A SIGNALIZED INTERSECTION WITH A HIGH NUMBER OF PEDESTRIAN CALLS (COURTESY COLLEGE STATION, TEXAS) 75 EXHIBIT 3-24. PEDESTRIAN DELAY EXAMPLE: PEDESTRIAN DELAY AT A FULLY ACTUATED, NON-COORDINATED SIGNAL (HUBBARD, BULLOCK, AND DAY 2008) 76 EXHIBIT 3-25. PEDESTRIAN CONFLICTS EXAMPLE: RIGHT-TURN VEHICULAR FLOW RATES DURING CYCLES WITH PEDESTRIAN ACTUATIONS (HUBBARD, BULLOCK, AND DAY 2008) 78 EXHIBIT 3-26. YELLOW/RED ACTUATIONS EXAMPLE: 24-HOUR YELLOW/RED ACTUATIONS (TAYLOR 2016) 81 EXHIBIT 3-27. RED-LIGHT-RUNNING OCCURRENCE CALCULATION USING STOP BAR DETECTOR OCCUPANCY 83 28 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 3-28. RED-LIGHT-RUNNING (RLR) OCCURRENCES EXAMPLE: COUNT OF RLR BEFORE AND AFTER SPLIT ADJUSTMENT (LAVRENZ ET AL. 2016) 84 EXHIBIT 3-29. EFFECTIVE CYCLE LENGTH EXAMPLE: EFFECTIVE CYCLE LENGTHS USING AN ADAPTIVE SYSTEM DURING A 6-MONTH PERIOD (RICHARDSON ET AL. 2017) 87 EXHIBIT 3-30. RELATIONSHIP BETWEEN PLATOON RATIO AND ARRIVAL TYPE (HCM 6TH EDITION) 89 EXHIBIT 3-31. PROGRESSION QUALITY EXAMPLE: PERCENT ON GREEN (POG) OVER A 1-MONTH PERIOD (DAY, BULLOCK ET AL. 2016) 90 EXHIBIT 3-32. PROGRESSION QUALITY EXAMPLE: OFFSET ADJUSTMENT IMPACT ON PERCENT ON GREEN (POG) (MACKEY 2017) 91 EXHIBIT 3-33. PURDUE COORDINATION DIAGRAM EXPLANATION (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 93 EXHIBIT 3-34. PURDUE COORDINATION DIAGRAM EXAMPLE: OFFSET ADJUSTMENT (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 95 EXHIBIT 3-35. CYCLIC FLOW PROFILE COMPUTATION EXPLANATION (DAY AND BULLOCK 2011) 98 EXHIBIT 3-36. CYCLIC FLOW PROFILE EXAMPLE: CORRIDOR APPLICATION (DAY AND BULLOCK 2011) 99 EXHIBIT 3-37. OFFSET ADJUSTMENT DIAGRAM EXPLANATION (DAY AND BULLOCK 2017) 101 EXHIBIT 3-38. EXAMPLE OFFSET ADJUSTMENT DIAGRAM: PERCENT ON GREEN (POG) ASSESSMENT FOR FIVE-INTERSECTION CORRIDOR (DAY AND BULLOCK 2017) 103 EXHIBIT 3-39. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: IMPACT OF OFFSET ADJUSTMENTS ON SPEED (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) 106 EXHIBIT 3-40. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: CORRIDOR RANKING USING TRAVEL TIME DATA (MATHEW ET AL. 2017) 107 EXHIBIT 3-41. TIME-SPACE DIAGRAM EXAMPLE: TIME-SPACE DIAGRAM FROM THE CLARK COUNTY, WASHINGTON, ATMS SYSTEM SHOWING ACTUAL GREEN BANDS (COURTESY CLARK COUNTY, WASHINGTON) 109 EXHIBIT 3-42. PREEMPTION DETAILS EXAMPLE: DETAILS WITH DETECTOR OCCUPANCY (BRENNAN ET AL. 2009) 112 EXHIBIT 3-43. PRIORITY DETAILS EXAMPLE: TRANSIT SIGNAL PRIORITY (MACKEY 2016) 114 EXHIBIT 3-44. PRIORITY DETAILS EXAMPLE: BUS RAPID TRANSIT CORRIDOR (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) 115 PERFORMANCE MEASURE DETAILS 29

CHAPTER FOCUS Chapter 3 contains detailed information for 26 signal performance measures. After practitioners have applied the selection process introduced in Chapter 2, they should use the information in this chapter to learn more about the signal performance measures that have been chosen (i.e., required inputs, resulting outputs, example applications, and additional references). This chapter organizes the signal performance measures that are listed and briefly described in EXHIBIT 3-1 and use the objective-based categories from Chapter 2: Communication Detection Intersection/ Uncoordinated Timing System/Coordinated Timing Advanced Systems and Applications 1 2 4 3 5 30 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 3-1 provides descriptions of the 26 signal performance measures and list example uses. EXHIBIT 3-2 summarizes inputs and outputs for all 26 signal performance measures in a single reference table. Following EXHIBIT 3-2, the balance of Chapter 3 provides detailed profiles for each measure. The profiles collect key information (including, as needed, cross references to information in later guidebook chapters) as summarized here: DESCRIPTION Detailed description of the performance measure and a brief overview of how it is calculated and displayed. APPLICATIONS How the performance measure can ultimately be applied (Chapter 5). STAKEHOLDERS Agency groups that may apply the performance measure – organizational, planning, design and construction, operations, and maintenance (Chapter 6). OBJECTIVES Traffic signal system objectives informed by the performance measure – equipment health, vehicle delay, vehicle progression, pedestrians, bicycles, rail, emergency vehicles, transit, trucks, and safety (Chapter 2). DATA SOURCES Data that can be used to produce the performance measure (Chapter 4). CONTROLLER HIGH- RESOLUTION DATA • Timestamped “events” recorded by the controller. CENTRAL SYSTEM LOW- RESOLUTION DATA • Volumes, detector occupancies, green times, and phase terminations aggregated by the central system. VENDOR-SPECIFIC DATA • Data collected by detection systems, preemption systems, and adaptive control systems. AUTOMATED VEHICLE IDENTIFICATION (AVI) • Travel time, route choice, and origin-destination estimated through unique vehicle identifiers. PROBE VEHICLE SEGMENT SPEED • Average speeds aggregated using data from probe vehicles. AUTOMATED VEHICLE LOCATION (AVL) • Timestamped GPS coordinates of probe vehicles. DETECTION NEEDS Type of detection needed (if any) for implementation (Chapter 4). CALIBRATION Some performance measures have calibration considerations; brief notes on those are included where relevant. REFERENCES Resources for further information. EXAMPLE USES Illustrative examples demonstrating how the performance measure can be used. PERFORMANCE MEASURE DETAILS 31

EXHIBIT 3-1. SIGNAL PERFORMANCE MEASURE DESCRIPTIONS CO M M UN IC AT IO N DE TE CT IO N IN TE RS EC TIO N/ UN CO OR DI NA TE D T IM IN G SY ST EM /C OO RD IN AT ED TI MI NG AD VA NC ED SY ST EM S A ND AP PL ICA TIO NS PERFORMANCE MEASURE DESCRIPTION EXAMPLE USE(S) 1 2 3 4 5 “3.1 COMMUNICATION STATUS” Reports controller online/ offline status. • Which corridor has the greatest need for communication investments? • Have maintenance efforts improved communication? 1 2 3 4 5 “3.2 FLASH STATUS” Reports intersections that have experienced unscheduled flash events. • Do any intersections consistently experience unscheduled flash events? 1 2 3 4 5 “3.3 POWER FAILURES” Reports intersections that have experienced power failures. • Are any corridors experiencing consistent power failures? 1 2 3 4 5 “3.4 DETECTION SYSTEM STATUS” Reports detector failures. • Are any corridors experiencing consistent detection issues? • Have maintenance efforts improved detection? 1 2 3 4 5 “3.5 VEHICLE VOLUMES” Reports the number of vehicles (or bicycles depending on available detection) making individual movements. • When are the peak traffic periods and what is their duration? • How were traffic volumes impacted during a special event? 1 2 3 4 5 “3.6 PHASE TERMINATION” Reports phase termination types (i.e., max-outs, force- offs, gap-outs, skips). • Do any phases need an adjustment to green time? 1 2 3 4 5 “3.7 SPLIT MONITOR” Reports phase duration compared to programmed split (along with phase termination type). • Are the splits programmed in a special event plan adequately serving traffic? 1 2 3 4 5 “3.8 SPLIT FAILURES” Reports the number of split failures (i.e., when there were unserved vehicles). • Are there corridors that can benefit from split adjustments? During which time periods? • Did implementation of an adaptive cycle length improve the number of split failures? 1 2 3 4 5 “3.9 ESTIMATED VEHICLE DELAY” Estimates delay of vehicles (or bicycles depending on available detection). • Did split adjustments improve vehicle delay? 1 2 3 4 5 “3.10 ESTIMATED QUEUE LENGTH” Estimates length of vehicle queues. • During what times of day is an approach experiencing long queues? 1 2 3 4 5 “3.11 OVERSATURATION SEVERITY INDEX” Estimates the prevalence of temporal and spatial oversaturation. • Did signal timing adjustments improve oversaturated conditions (i.e., downstream blockages and split failures)? 1 2 3 4 5 “3.12 PEDESTRIAN VOLUMES” Reports the number of pedestrians making individual movements. • What is the pedestrian demand during different times of day? 1 2 3 4 5 “3.13 PEDESTRIAN PHASE ACTUATION AND SERVICE” Reports the number of times that pedestrian phases were actuated and served. • What times of day have high pedestrian actuations (and resulting pedestrian phase service)? • Will an exclusive pedestrian phase impact how often pedestrians request service (using the pedestrian push button)? • Which locations (i.e., signalized intersections and corridors) have high rates of pedestrian activity? 32 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXHIBIT 3-1. SIGNAL PERFORMANCE MEASURE DESCRIPTIONS (CONTINUED) CO M M UN IC AT IO N DE TE CT IO N IN TE RS EC TIO N/ UN CO OR DI NA TE D T IM IN G SY ST EM /C OO RD IN AT ED TI MI NG AD VA NC ED SY ST EM S A ND AP PL ICA TIO NS PERFORMANCE MEASURE DESCRIPTION EXAMPLE USE(S) 1 2 3 4 5 “3.14 ESTIMATED PEDESTRIAN DELAY” Reports the amount of time between pedestrian phase actuation to service. • Are there times of day when pedestrians are experiencing long delays? • What level of service (LOS) do pedestrians experience at a signalized intersection? 1 2 3 4 5 “3.15 ESTIMATED PEDESTRIAN CONFLICTS” Estimates potential for vehicle- to-pedestrian conflicts. • What are the highest conflicting vehicular flow rates across pedestrian crossings? 1 2 3 4 5 “3.16 YELLOW/RED ACTUATIONS” Reports vehicle actuations relative to the yellow and red times. • Are there times of day with high numbers of vehicles running the red light? 1 2 3 4 5 “3.17 RED-LIGHT- RUNNING (RLR) OCCURRENCES” Reports the total number of red-light-running vehicles. • Did a split increase result in a reduced number of red-light-running vehicles? 1 2 3 4 5 “3.18 EFFECTIVE CYCLE LENGTH” Reports the effective cycle length (amount of time used to serve all phases). • What are the seasonal impacts on effective cycle length for a corridor utilizing an adaptive system? 1 2 3 4 5 “3.19 PROGRESSION QUALITY” Reports percent on green, platoon ratio, and/or arrival type. • Are any intersections along a corridor experiencing lower progression quality? • Did offset adjustments increase or decrease progression quality along a corridor? 1 2 3 4 5 “3.20 PURDUE COORDINATION DIAGRAM” Shows individual vehicle arrival times relative to green intervals. • Did offset adjustments improve progression for a particular approach at an intersection? 1 2 3 4 5 “3.21 CYCLIC FLOW PROFILE” Reports the distribution of vehicle arrivals relative to the distribution of green. • How much and at which locations did offset adjustments improve progression along a corridor? 1 2 3 4 5 “3.22 OFFSET ADJUSTMENT DIAGRAM” Estimates potential progression quality using different offset adjustments. • What is the potential for progression improvement along a coordinated corridor? 1 2 3 4 5 “3.23 TRAVEL TIME AND AVERAGE SPEED” Measures or estimates vehicle travel times (or conversely, average speeds) on defined routes. • Did offset adjustments impact corridor speeds? • Where are the most critical intersections based on travel times and reliability? 1 2 3 4 5 “3.24 TIME-SPACE DIAGRAM” Can depict the actual signal timing at intersections; GPS trajectories potentially can be overlaid. • Is the signal timing resulting in expected green bands? 1 2 3 4 5 “3.25 PREEMPTION DETAILS” Reports timing information for individual preemption events. • Are vehicles being consistently cleared from the railroad tracks during the track clearance green interval? 1 2 3 4 5 “3.26 PRIORITY DETAILS” Reports timing information for individual priority events. • How many transit signal priority requests were made, and how were they served (i.e., early green or green extend)? • How often are priority requests being made on a bus rapid transit (BRT) corridor? PERFORMANCE MEASURE DETAILS 33

EXHIBIT 3-2. SIGNAL PERFORMANCE MEASURE INPUTS AND OUTPUTS PERFORMANCE MEASURE REQUIRED INPUTS POTENTIAL OUTPUTS DATA SOURCE DETECTION STAKEHOLDERS OBJECTIVES HI GH -R ES OL UT IO N LO W -R ES OL UT IO N VE ND OR -S PE CI FI C AV I/ SE GM EN T SP EE D/ AV L NO NE UN M AP PE D ST OP B AR P RE SE NC E ST OP B AR C OU N T AD VA NC E RA DA R SP EE D OR GA NI ZA TI ON AL PL AN NI NG DE SI GN A ND C ON ST RU CT IO N OP ER AT IO NS M AI N TE NA NC E EQ UI PM EN T HE AL TH VE HI CL E DE LA Y VE HI CL E PR OG RE SS IO N PE DE ST RI AN S BI CY CL ES RA IL EM ER GE NC Y VE HI CL ES TR AN SI T TR UC KS SA FE TY “3.1 COMMUNICATION STATUS” X X X X X X X X “3.2 FLASH STATUS” X X X X X X “3.3 POWER FAILURES” X X X X X X “3.4 DETECTION SYSTEM STATUS” X X X (1) X X X X “3.5 VEHICLE VOLUMES” X X X X X X X X X X X X “3.6 PHASE TERMINATION” X X X X X X X “3.7 SPLIT MONITOR” X X X X “3.8 SPLIT FAILURES” X X (2) (2) X X X “3.9 ESTIMATED VEHICLE DELAY” X X X (3) (3) (4) X X X X “3.10 ESTIMATED QUEUE LENGTH” X X X X (5) X X X X X “3.11 OVERSATURATION SEVERITY INDEX” X X X X X X “3.12 PEDESTRIAN VOLUMES” X X X X X X X X “3.13 PEDESTRIAN PHASE ACTUATION AND SERVICE” X X X X X X X X “3.14 ESTIMATED PEDESTRIAN DELAY” X X X X X “3.15 ESTIMATED PEDESTRIAN CONFLICTS” X X X X X X X X X “3.16 YELLOW/RED ACTUATIONS” X X (6) X X 34 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

PERFORMANCE MEASURE REQUIRED INPUTS POTENTIAL OUTPUTS DATA SOURCE DETECTION STAKEHOLDERS OBJECTIVES HI GH -R ES OL UT IO N LO W -R ES OL UT IO N VE ND OR -S PE CI FI C AV I/ SE GM EN T SP EE D/ AV L NO NE UN M AP PE D ST OP B AR P RE SE NC E ST OP B AR C OU N T AD VA NC E RA DA R SP EE D OR GA NI ZA TI ON AL PL AN NI NG DE SI GN A ND C ON ST RU CT IO N OP ER AT IO NS M AI N TE NA NC E EQ UI PM EN T HE AL TH VE HI CL E DE LA Y VE HI CL E PR OG RE SS IO N PE DE ST RI AN S BI CY CL ES RA IL EM ER GE NC Y VE HI CL ES TR AN SI T TR UC KS SA FE TY “3.17 RED-LIGHT-RUNNING (RLR) OCCURRENCES” X X X X “3.18 EFFECTIVE CYCLE LENGTH” X X (7) X X X X X “3.19 PROGRESSION QUALITY” X X X X “3.20 PURDUE COORDINATION DIAGRAM” X X X X “3.21 CYCLIC FLOW PROFILE” X X X X “3.22 OFFSET ADJUSTMENT DIAGRAM” X X X X “3.23 TRAVEL TIME AND AVERAGE SPEED” X X (8) (8) (8) X X X X “3.24 TIME-SPACE DIAGRAM” X X X X (9) X X X “3.25 PREEMPTION DETAILS” X X X X X X X “3.26 PRIORITY DETAILS” X X X X X X X EXHIBIT 3-2. SIGNAL PERFORMANCE MEASURE INPUTS AND OUTPUTS (CONTINUED) (1) Although some detection alarms do not require detection to be mapped, the most useful metrics will report status on specific detectors. (2) Stop bar count and advance detection can be used to calculate volume-to-capacity ratios. (3) Stop bar count and advance detection can be used to count vehicles for use in the HCM delay equation. Advance detection can also be used in arrival and departure models and to estimate time to service (with an adjustment to account for travel time to the stop bar). (4) AVI/AVL data can be used to estimate delay using a travel time “route” that covers only one signalized movement. (5) AVL data can be used to measure queues if the data are available at a high enough penetration rate. (6) Some detection technologies allow actuations to be recorded only if vehicles are traveling over a specific speed. (7) No detection is required, but without detection, cycle lengths will remain constant. (8) Stop bar presence, stop bar count, and advance detection can be used to calculate Estimated Vehicle Delay, which can be aggregated to estimate travel time. (9) AVL data can be used to overlay vehicle trajectories. PERFORMANCE MEASURE DETAILS 35

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.1 COMMUNICATION STATUS 1 2 43 5 CATEGORY STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify communication equipment that is malfunctioning. • Compare different types of communication. Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 36 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Communications technology can be used to connect equipment at a signalized intersection (i.e., controller, cameras, ITS equipment) both to other signalized intersections for coordination purposes and/or to a central system for monitoring. Communication is a critical element of performance-based management because it provides automatic access to real-time data. This metric reports the online/offline status of signalized intersections connected to a central system, which can be identified through: • An occasional (e.g., once every few minutes) “ping” of the controller. If the connection is not successful, an error message is typically recorded by the controller. • Availability of high-resolution data in a database. Gaps in data may imply the existence of extended times during which the controller was offline. • Vendor-specific reports. The intersection online/offline status data can be aggregated to identify time periods (e.g., days, weeks, months), locations (e.g., individual intersections, corridors, districts, networks), or communication types experiencing communication issues. None N/A EXAMPLE USE Which corridor has the greatest need for communication investments? Have maintenance efforts improved communication? EXHIBIT 3-3 summarizes the communication status of signalized intersections connected by cell modem in an Indiana district. The total number of connected intersections is represented by the dashed line, and the bars illustrate the number of signalized intersections along each corridor (differentiated by color) for which data was not found on a particular day over the course of a month. This chart was used to identify which corridors should be the focus of communication upgrade efforts. An improvement made along US-36 halfway through the month resulted in additional online intersections. All Signals Offline Communication Improvement Along US-36 EXHIBIT 3-3. COMMUNICATION SYSTEM STATUS EXAMPLE: NUMBER OF SIGNALIZED INTERSECTIONS BY CORRIDOR OFFLINE OVER 1 MONTH (DAY, BULLOCK ET AL. 2016) SR-25 US-231 US-40 SR-334 US-36 Total High- Resolution Signals • Day et al. (2014) • Day, Bullock et al. (2016) • Li et al. (2013) PERFORMANCE MEASURE DETAILS 37

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.2 FLASH STATUS 1 2 43 5 CATEGORY STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections operating in flash. • Determine frequency and duration of flash events to identify cause(s). Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 38 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Flash is a mode of operation that effectively turns a traffic signal into a two-way or four-way stop-controlled intersection by flashing the traffic signal displays (either yellow or red) at a constant rate. Some agencies use flash at night for low- volume intersections, but it is often applied if there is a problem at the intersection (e.g., power failure, controller malfunction, conflicting phases or indications). This metric reports how often intersections operate in flash mode based on: • Flash status flagged as alarms by a central system. • Flash status flagged by the controller itself and logged in the high-resolution data. Flash status data can be aggregated to identify specific time periods (e.g., days, weeks, months) and specific locations (e.g., individual intersections, corridors, districts, networks) experiencing unscheduled flash events. DETECTION NEEDS CALIBRATION REFERENCES None N/A N/A EXAMPLE USE Do any intersections consistently experience unscheduled flash events? EXHIBIT 3-4 shows the number of flash events recorded over 2 months using high-resolution data from a 15-intersection corridor. The data are shown by hour of the day, with each color representing a different intersection. The chart highlights that Pendleton Pike @ I-465 northbound (NB) has a number of flash events spread out over the day. A flash event is written at the “23” hour at every intersection every day, which probably does not represent an actual flash event. This chart was used to identify intersections for closer inspection and troubleshooting. EXHIBIT 3-4. FLASH STATUS EXAMPLE: NUMBER OF FLASH EVENTS PER INTERSECTION BY HOUR OF THE DAY OVER 2 MONTHS (COURTESY PURDUE UNIVERSITY) Event Recorded Every Day at Every Intersection Dark Blue Bars Indicate Consistent Flash Events at the I-465 NB Ramps Pendleton Pike @ Super Walmart Pendleton Pike @ Sunnyside Rd Pendleton Pike @ Post Rd Pendleton Pike @ Oaklandon Rd Pendleton Pike @ Mt Comfort Rd/600 W Pendleton Pike @ Monarch Beverage Co Pendleton Pike @ Mitthoeffer Rd Pendleton Pike @ 1-465 SB Ramps Pendleton Pike @ 1-465 NB Ramps Pendleton Pike @ Franklin Rd Pendleton Pike @ Esquire Plaza/Mowery St Pendleton Pike @ Carroll Rd Pendleton Pike @ 65 St Pendleton Pike @ 56 St Pendleton Pike @ 42 St PERFORMANCE MEASURE DETAILS 39

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.3 POWER FAILURES STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify power equipment that is malfunctioning. • Identify locations that can benefit from back-up power supply (BPS) systems. • Identify battery life typically required for BPS systems. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 40 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Power failures can cause traffic signal outages, so agencies sometimes install back-up power supply (BPS) systems to allow traffic signal controllers to continue operating for a period of time. Information about the locations and durations of power failures can be used to determine where BPS systems should be installed as well as the typical battery life required. This metric reports how often there are power failures at an intersection based on: • Power failures flagged as alarms by a central system. • Power failures flagged by the controller itself and logged in the high-resolution data. Data about power failure events can be aggregated to identify specific time periods (e.g., days, weeks, months) and specific locations (e.g., individual intersections, corridors, districts, networks) experiencing power failures. DETECTION NEEDS CALIBRATION REFERENCES None N/A • Zhao et al. (2015) EXAMPLE USE Are any corridors experiencing consistent power failures? EXHIBIT 3-5 shows the number of power failure events logged over 6 months for nine corridors in Indiana. Spikes during the weeks of 3/29/2016 and 6/21/2016 can be correlated with severe weather events that passed through those areas. However, one corridor (US-36 in Avon) experienced a number of power failures during the week of 5/29/2016 for which an explanation was not immediately available. It is possible that maintenance activity on the corridor necessitated power cycling of several intersections during the course of the day. Overall, none of the corridors experienced consistent power failures during the 6-month period. EXHIBIT 3-5. POWER FAILURES EXAMPLE: NUMBER OF POWER FAILURE EVENTS BY CORRIDOR OVER 6 MONTHS (COURTESY PURDUE UNIVERSITY) Weather Events Power Failures to Investigate on US-36 in Avon US-421 Zionsville US-36 Avon US-31 Greenwood US-31 Columbus US-231 Greater Lafayette SR-37 Noblesville SR-37 Martinsville SR-37 Indianapolis South Pendleton Pike PERFORMANCE MEASURE DETAILS 41

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.4 DETECTION SYSTEM STATUS STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify detection equipment that is malfunctioning. • Compare different types of detection. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 42 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Detection is used at a signalized intersection to determine the presence of transportation system users, so that the traffic signal controller can allocate right-of-way safely and efficiently. Broken detection typically defaults “on” to prevent users from being skipped, but this can result in inefficiencies when a phase receives more time than needed. This metric reports the number of detector failures based on: • Detector failures flagged as alarms by a central system or in vendor-specific reports. • Detector failures flagged by the controller itself and logged in the high-resolution data. • Anomalies identified through statistical analysis of the number of actuations over time (i.e., comparing the number of actuations to historical data to determine if they are “high or low”). • Observations of a high amount of cycling during time periods when low traffic is expected (e.g., phases maxing out late at night). The number of failed detectors can be tracked for specific time periods (e.g., days, weeks, months) and specific locations (e.g., individual intersections, corridors, districts, networks). A list of currently broken detectors can serve as a tool for deciding where to invest maintenance efforts, while a history of broken detection can be used to evaluate the overall detection program. • Day et al. (2014) • Day, Bullock et al. (2016) • Lavrenz (2015) DETECTION NEEDS CALIBRATION REFERENCES This metric will report on existing detection; no additional detection is necessary. N/A EXAMPLE USE Are any corridors experiencing consistent detection issues? Have maintenance efforts improved detection? EXHIBIT 3-6 depicts the number of side-street phases on several corridors being served every cycle during the overnight hours. This is an indicator of detector failures, which may be causing the associated phases to max out every cycle. This chart was used to identify eight corridors consistently affected in this way during a 4-month period as well as the impact of maintenance activities. For example, numerous detector issues on US-421 were corrected the week of 12/3/2014, and detector issues on SR-14 were corrected around 12/17/2014. EXHIBIT 3-6. DETECTION SYSTEM STATUS EXAMPLE: NUMBER OF SIDE-STREET PHASES SERVED EVERY CYCLE LATE AT NIGHT (INDICATING FAILED DETECTORS) PER CORRIDOR OVER 4 MONTHS (DAY, BULLOCK ET AL. 2016) SR-14 Detection Improvement US-421 Detection Improvement US-52 US-421 US-41 US-40 US-36 US-31 US-30 US-24 US-231 SR-60 SR-53 SR-37 SR-334 SR-32 SR-311 SR-261 SR-252 SR-25 SR-14 SR-135 SR-111 I-70 I-69 I-465 PERFORMANCE MEASURE DETAILS 43

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.5 VEHICLE VOLUMES STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify time-of-day plan adjustments. • Identify intersections with high vehicle volumes (which can be compared to capacity). • Identify intersections with high bicycle volumes (depending on available detection). • Identify detection equipment that is malfunctioning. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 44 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Volume data can be useful when programming signal timing values or troubleshooting detection issues, and is also often collected for planning purposes. This metric reports the number of vehicles (or bicycles, depending on available detection) observed in a lane or on an approach. The data may be shown as a pure count over a time interval (e.g., 15-minute counts) or on a cycle-by-cycle basis. The number of vehicles is often normalized to a flow rate (e.g., vehicles per hour). This is especially important for cycle-by-cycle counts because each (effective) cycle may not have the same duration as the others. The conversion is: (Flow Rate, vehicles per hour) = 3,600 x (Vehicles Counted) ÷ (Counting Interval, seconds) Signal timing adjustments. Volume profiles can be valuable for evaluating when signal timing plans should begin and end throughout the day. Lane volumes can be used to validate signal phasing and some signal timing parameters (e.g., using a critical movement analysis to assess capacity and adjust splits). If bicycle counts are available (i.e., if bicycle detection is separate from vehicle detection), signal timing parameters can be tailored to bicyclists at intersections with high bicycle volumes (e.g., extended clearance intervals and minimum green times). Detection troubleshooting. Volumes can also be used to identify detection equipment that is malfunctioning. Because this metric often allows a disaggregated look at volumes by individual detector, individual lane, or approach, volumes that are higher or lower than historical averages can be used to identify broken detectors. Planning. Turning movement counts can be used for microscopic models, and approach volumes can help validate macroscopic model assumptions. Although high- resolution data reduces the need to create models for signal retiming, models may be helpful for efforts such as planning for major geometric changes. Numerous secondary metrics applicable to planning studies can be determined using vehicle counts, such as peak hour factors, directional splits, and K-factors. A variety of detection schemes can be used to gather volume data. Small detection zones capable of detecting a single vehicle will provide the most accurate counts. The detectors should be placed either in advance of the intersection (upstream of typical queues) or past the stop bar. For lane-by-lane counts, detection zones located in each lane require their own input channel. When multiple detection zones across lanes report to a single channel, the total number of vehicles may be higher than the actuations recorded (e.g., if two vehicles pass over the detectors at the same time). For turning movement counts, detection zones can be set up past the stop bar as well as in the outbound lanes of the intersection. At some locations, detectors for certain movements may not be available. It is possible to estimate the count by using a percentage of some other, related detector. For example, an advance detector may capture traffic heading to several movements on an approach. If there is no detector in place to capture a right-turn movement, it can be estimated using an assumed percentage of the advance detector count. The accuracy of this method depends on the reliability of the percentage applied. • Day et al. (2008) • Day and Bullock (2010) • Day et al. (2014) • ITE (2008) • UDOT PERFORMANCE MEASURE DETAILS 45

EXAMPLE USE 1 When are the peak traffic periods and what is their duration? EXHIBIT 3-7 shows northbound and southbound flow rates (calculated using 15-minute volumes) that illustrate the directionality along a commuter corridor in Virginia. These volume profiles can be compared to the time-of-day plan to assess whether timing plans start and end at appropriate times. Additionally, planning-level metrics can be calculated using the same volume data, which can then be used to calibrate any models developed for the corridor. EXHIBIT 3-7. VOLUMES EXAMPLE: VOLUME PROFILES AND PLANNING METRICS (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) METRIC VALUE Peak Hour 2/12/2018 4:45:00 PM Peak Hour Factor 0.346 Peak Hour Volume 11620 Peak Hour Factor 0.989 Total Volume 33586 Northbound Peak Hour 5:00 PM - 6:00 PM Northbound Peak Hour D Value 0.747 Northbound Peak Hour K Value 0.407 Northbound Peak Hour Volume 6572 Northbound Peak Hour Factor 0.976 Southbound Peak Hour 7:30 AM - 8:30 AM Southbound Peak Hour D Value 0.543 Southbound Peak Hour K Value 0.405 Southbound Peak Hour Volume 7068 Southbound Peak Hour Factor 0.956 Northbound Southbound 46 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE 2 How were traffic volumes impacted during a special event? During the total solar eclipse on August 21, 2017, the Oregon Department of Transportation (ODOT) closely monitored traffic volumes (shown in EXHIBIT 3-8). Volumes dropped significantly right before and during the event but returned to normal traffic patterns relatively quickly. EXHIBIT 3-8. VOLUMES EXAMPLE: VOLUME PROFILES DURING A SPECIAL EVENT (COURTESY OREGON DEPARTMENT OF TRANSPORTATION) Traffic Volumes Dropped During the Eclipse Event Northbound Southbound PERFORMANCE MEASURE DETAILS 47

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.6 PHASE TERMINATION STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify phases that potentially require an adjustment to green time (proxy for split failures). • Identify detection equipment that is malfunctioning. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 48 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Actuated phases terminate either because there is a gap in traffic or because the phase has reached its maximum programmed time. This metric reports the reason that individual phases terminated (i.e., a gap-out, max- out, force-off, or skip). This information can typically be gathered: • For each cycle using high-resolution data from the controllers. • As aggregated data for specified time periods from a central system (e.g., number of gap-outs, max-outs, force-offs, and skips during a 15-minute interval). This metric is useful for determining times of day when a phase is repeatedly using all its allocated green time. While coordinated phases (except when actuated) are forced off at their programmed time to yield to other phases, non-coordinated phases that repeatedly force off are likely experiencing heavy demand or perhaps a constant call from a faulty detector. This metric is beneficial at locations with existing detection; no additional detection is necessary. N/A • Day et al. (2014) • Li et al. (2013) PERFORMANCE MEASURE DETAILS 49

EXAMPLE USE Do any phases need an adjustment to green time? EXHIBIT 3-9 shows the distribution of phase termination types aggregated into 30-minute bins, rather than for individual cycles, for each phase at an intersection. The data includes phase skips, gap-outs, and combined max-outs and force-offs. Phases 2 and 6 are shown forcing off frequently during the day because they are coordinated; note that these phases gap out on occasion due to the use of early yield. High numbers of max-outs and force-offs can generally be correlated to heavy demand during peak periods. However, Phase 3 is a candidate for closer inspection because it forces off frequently throughout most of the day. This can indicate that the phase requires an adjustment to green time. EXHIBIT 3-9. PHASE TERMINATION EXAMPLE: DISTRIBUTION OF TERMINATION TYPES BY PHASE (DAY ET AL. 2014) Max-Outs/Force-Offs Expected on Coordinated Phases 2 and 6 Phase 3 Max-Outs/Force-Offs During a High Percentage of Cycles Skip Gap-Out Max-Out or Force-Off 50 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.7 SPLIT MONITOR STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify phases that potentially require an adjustment to green time (proxy for split failures). • Identify amount of green time adjustment (i.e., time to add or subtract). 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s PERFORMANCE MEASURE DETAILS 51

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES This metric is used to report detailed information about the performance of an individual phase. Using high-resolution data, it combines a plot of phase duration with several other pieces of information– termination type, pedestrian phase service, and programmed splits. This metric is useful for assessing whether signal timing parameters have been programmed correctly, how much of the programmed split is being used, and whether signal timing adjustments had an impact. The pattern change information can also be used to infer events such as interruption of a pattern by preemption or priority control. This metric is beneficial at locations with existing detection; no additional detection is necessary. N/A • Mackey (2017) • UDOT EXAMPLE USE Are the splits programmed in a special event plan adequately serving traffic? A freeway detour greatly increased northbound left-turn volumes at a signalized intersection in Cedar City, Utah. In September 2014, heavy rain in Nevada destroyed a portion of I-15, forcing the closure of the route. Traffic was diverted, and the first signalized intersection along the route was the exit ramp from I-15 (shown in EXHIBIT 3-10). EXHIBIT 3-11 depicts the split monitor data for the associated northbound left-turn phase during the week of the detour. A new timing plan was implemented (with increased green time for the northbound left-turn phase) to accommodate diverted traffic. The split monitor chart does show that some force-off events occurred, but many more gap-outs were observed during the same time period. This indicates the northbound left-turn phase typically utilized less time than the new split. Had the original programmed split been in place, the force-off events would have increased significantly, potentially causing traffic to queue onto the freeway. EXHIBIT 3-10. SPLIT MONITOR EXAMPLE: DETOUR ROUTE EXHIBIT 3-11. SPLIT MONITOR EXAMPLE: NORTHBOUND LEFT- TURN PHASE AFFECTED BY I-15 CLOSURE (MACKEY 2017) Phase Utilizing Additional Time New Split for Detour Original Programmed Split Gap-Out Max-Out Force-Off Unknown Pedestrian Activity 52 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.8 SPLIT FAILURES STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify phases and/or intersections experiencing split failures (i.e., requiring adjustments to green time or detection settings). 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s PERFORMANCE MEASURE DETAILS 53

DESCRIPTION A “split failure” is an occurrence when there are unserved vehicles at the end of green. A phase that has multiple consecutive split failures is very likely to have an operational problem that can potentially be fixed by increasing the split (or the max time under fully actuated control) or adjusting detection settings (i.e., passage time). There are different ways to estimate the number of split failures depending on the type of detection available: Occupancy ratios. Using stop bar presence detection (lane-by-lane is most effective), the green occupancy ratio (GOR) and red occupancy ratio (ROR) can be calculated. This is the percentage of the green interval or the first few seconds of the red interval during which the detector is occupied. When both GOR and ROR are above a threshold (i.e., 80%), the phase is likely to have had a split failure. GOR = (Total Occupancy Time During Green, seconds) / (Total Green Time, seconds) ROR = (Total Occupancy Time During the First x Seconds of Red, seconds) ÷ x where x = Selected amount of time for confirming a split failure, seconds (i.e., 5 seconds) For GOR and ROR calculations, the yellow interval is not taken into consideration; however, a yellow occupancy ratio (YOR) can be calculated separately. Volume-to-capacity ratios. Using advance detection or stop bar count detection, the volume-to-capacity (v/c) ratio can be calculated. The capacity should be estimated using the saturation flow rate, but volumes can be calculated directly through vehicle actuations. When the v/c ratio exceeds a threshold, the phase is likely to have had a split failure. Planners may also be interested in the calculated v/c ratios. v/c ratio = (3,600 x N) ÷ (s x g) where N = Number of vehicles counted in an effective green interval and the preceding effective red interval (as shown in EXHIBIT 3-12) s = Saturation flow rate, vehicles per hour g = Green time, seconds With advance detection, the vehicle actuation times should be adjusted to account for travel time from the detector to the stop bar, as discussed further in Section 3.19: Progression Quality. With stop bar count detection, the vehicle actuation times can be used without such an adjustment. Note that the start of effective red and the start of effective green differ from the actual start of red and start of green due to start-up lost time and utilization of yellow. Historically, the Highway Capacity Manual (HCM) value of 2 seconds has been used (although different values may be used if warranted), yielding the following definitions: (Start of Effective Green) = (Actual Start of Green) + 2 seconds (Start of Effective Red) = (Actual End of Green) + 2 seconds The number of split failures can be reviewed for individual phases, the entire intersection, or across a corridor. The underlying metric (GOR/ROR or v/c ratio) can also be examined in more detail if desired. EXHIBIT 3-12. VOLUME-TO-CAPACITY V/C RATIO CALCULATION 54 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DETECTION NEEDS REFERENCES CALIBRATION • For calculating GOR and ROR, stop bar presence detection is required. Lane-by-lane detection provides more accurate results than detectors tied together across lanes; multi- lane detectors may over-estimate occupancy ratios. • For calculating volume-to-capacity ratios, either advance detection or stop bar count detection is required. • Day et al. (2008) • Day et al. (2014) • Day, Bullock et al. (2016) • Freije et al. (2014) • Li et al. (2017) • Richardson et al. (2017) • Smaglik, Sharma et al. (2007) • Smaglik et al. (2011) • If desired, the number of consecutive split failures may be used to identify locations requiring further investigation, but a threshold will need to be determined. • Occupancy ratios. For GOR and ROR, the threshold above which to consider a phase to have a split failure is a parameter requiring calibration; previous research has used a value of 80%. Additionally, the amount of red time used for ROR is a parameter requiring calibration; previous research has used a value of five seconds. • Volume-to-capacity ratios. For v/c ratios, both the threshold for split failures as well as the saturation flow rate require calibration. Previous research has used a threshold of 1.0 to define split failures assuming a saturation flow rate of 1,900 vehicles/hour/lane, but lower or higher values should be used to reflect site conditions. Typical saturation flow rates are between 1,800–2,100 vehicles/hour/lane with higher values usually observed in denser areas. Refer to Performance Measures for Traffic Signal Systems: An Outcome- Oriented Approach for more information (Day et al. 2014). If advance detection is used, an adjustment should be made to the vehicle detection times to estimate the time each vehicle arrives at the stop bar. This can be done by adding the travel time to the detection time. For example, if the detector is positioned 5 seconds upstream of the stop bar, 5 seconds would be added to the reported actuation times. The actual red and green intervals at the intersection would typically be converted into “effective red” and “effective green” times. The determination of effective red and green times should also reflect site conditions. PERFORMANCE MEASURE DETAILS 55

EXAMPLE USE 1 Are there corridors that can benefit from split adjustments? During which time periods? EXHIBIT 3-13 summarizes the number of split failures per hour that occurred along seven corridors by day of the week and time of day. The values greater than 10 split failures per hour are highlighted as a way to quickly identify locations and time periods potentially requiring split adjustments. The high rates occurring during the PM peak are expected. However, US-31 Greenwood shows a high rate of split failures on Saturday when its weekday rates are generally lower. The US-31 corridor may benefit from more detailed investigation of splits programmed in the Saturday plan. EXHIBIT 3-13. SPLIT FAILURES EXAMPLE: NUMBER OF SPLIT FAILURES PER HOUR FOR SEVEN CORRIDORS (LI ET AL. 2017) US-31 Corridor Experiencing High Split Failures on Saturday Compared to Weekdays High Split Failures During PM Peak Are Expected 56 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE 2 Did implementation of an adaptive cycle length improve the number of split failures? EXHIBIT 3-14 depicts the number of split failures along a five-intersection corridor before and after implementation of an adaptive cycle length. Because of the corridor’s proximity to national parks in Moab, Utah, traffic volumes fluctuate intensely and unpredictably, making the location ideal for an adaptive solution. EXHIBIT 3-14 shows the number of split failures by phase with each intersection shown in a different color. The adaptive algorithm led to an overall reduction in split failures, particularly on Phases 2 and 6, without worsening the performance for any particular phase. EXHIBIT 3-14. SPLIT FAILURES EXAMPLE: NUMBER OF SPLIT FAILURES BEFORE AND AFTER IMPLEMENTATION OF AN ADAPTIVE CYCLE LENGTH ALGORITHM (RICHARDSON ET AL. 2017) Phase 2 (Northbound) Split Failures Decreased on Phase 2 Marginal Increase in Split Failures on Phase 4 Split Failures Decreased on Phase 6 Phase 6 (Southbound) Phase 4 (Eastbound) Phase 8 (Westbound) PERFORMANCE MEASURE DETAILS 57

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.9 ESTIMATED VEHICLE DELAY STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify phases/intersections with high vehicle delay. • Identify phases/intersections with high bicycle delay (depending on available detection). 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 58 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Vehicle delay is a metric that is commonly modeled by agencies to identify whether intersection operations are acceptable. Using high-resolution data, this metric can be computed directly. For locations with high delay, particularly at uncongested locations, signal timing adjustments can help reduce wait times (e.g., through changes to cycle length, split, or phase order). This metric is used to report the amount of delay experienced by vehicles (or bicycles depending on available detection) at signalized intersections. Delay is typically expressed as an average over a signal cycle or an interval (e.g., 5-minute, 15-minute). There are numerous ways to estimate or measure it: • Arrival and departure model. The “input- output” method builds a queue profile by considering the measured arrival times of vehicles and their expected departure times. The “area” of the queue over time represents the delay. These methods require an advance detector to measure arrival times. EXHIBIT 3-15. MAXIMUM VEHICLE DELAY AND TIME TO SERVICE CALCULATIONS • Highway Capacity Manual (HCM) model based on measured traffic volumes and green times. • “Maximum vehicle delay,” which can be estimated as the time between the first “on” time of the stop bar detector and the next time that the detector turns “off” during a green interval (illustrated in EXHIBIT 3-15 using the phase state and stop bar detector occupancy state). • “Time to service,” which is the time from the first call to the start of green (illustrated in EXHIBIT 3-15). This measure will be most accurate with stop bar detection, but an advance detector can be used to estimate time to service as long as an adjustment is made for the time to travel between the advance detector and the stop bar. • Travel time “routes” that cover only one signalized movement (similar to travel time techniques discussed in Section 3.23: Travel Time and Average Speed). PERFORMANCE MEASURE DETAILS 59

DETECTION NEEDS CALIBRATION REFERENCES • Arrival and departure models require advance detection to measure arrival times. • HCM model requires detection that is capable of counting volumes (refer to Section 3.5: Vehicle Volumes). • Maximum vehicle delay and time to service require stop bar presence detection to estimate the time between when the first vehicle stops and is then served. Time to service can also be estimated if there is advance detection, but an adjustment must be made to account for the travel time between the advance detector and the stop bar. Additional details are available in the references. • Day and Bullock (2010) • Day et al. (2014) • Lavrenz et al. (2015) • Sharma, Bullock, and Bonneson (2007) • Smith (2014) • Sunkari, Charara, and Songchitruska (2012) 60 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE Did split adjustments improve vehicle delay? EXHIBIT 3-16 shows cumulative distributions of maximum vehicle delay for eight phases before and after split adjustments. Cumulative distribution charts report the percentile of cycles for which vehicles are experiencing different durations of delay. For example, the median delay (50th percentile) before the split adjustment on Phase 3 was approximately 100 seconds, and after the split adjustment, the median delay shifted to approximately 80 seconds. In this example, green time was moved from Phases 2 and 6 to Phases 3 and 8. As a result, delay decreased on Phases 3 and 8, while Phases 2 and 6 experienced little change in delay. This chart confirmed that the split adjustment improved overall intersection delay. Beyond median delay, the highest delays are also important to consider. When vehicles are experiencing delay above the cycle length (represented by the dashed lines), it can be an indicator of cycle failures (when there are unserved vehicles). In addition to improved median delay on Phase 3, the percentile of cycles with delay above the cycle length also improved (from approximately the 80th percentile to the 90th percentile). EXHIBIT 3-16. ESTIMATED DELAY EXAMPLE: CUMULATIVE DISTRIBUTIONS OF MAXIMUM VEHICLE DELAY FOR EIGHT PHASES BEFORE AND AFTER SPLIT ADJUSTMENT (LAVRENZ ET AL. 2015) Reduced Number of Cycles with Delay Above Cycle Length Additional Green Time Reduced Delay on Phase 8 Reduced Green Time for Phase 6 Resulted in Minimal Change to Delay Additional Green Time Reduced Delay on Phase 3 Reduced Green Time for Phase 2 Resulted in Minimal Change to Delay PERFORMANCE MEASURE DETAILS 61

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.10 ESTIMATED QUEUE LENGTH STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify locations and durations of long queues. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 62 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Long queues can interfere with progression, increase vehicle delay, and cause safety issues. Queue management is particularly important at tightly spaced intersections, interchanges, and congested locations. This metric reports the queue length for signalized movements, which can be used to identify signal timing adjustments as well as inform geometric design decisions (e.g., turn bay lengths). Several methods can be used to estimate or measure queue length: • Arrival and departure model. Analyzes vehicle arrival and discharge patterns using an advance detector. An “input- output” method can be developed that counts vehicle arrivals on red and estimates the size of the queue (Sharma, Bullock, and Bonneson 2007). It is then assumed that vehicles discharge at the saturation flow rate. • Advance detector occupancy. Uses advance detector occupancy as empirical evidence that the queue has extended to the detector, and then combines this with a traffic model to develop a queue estimate that can be calculated even if queues reach beyond the advance detector (Liu et al. 2008). • Stop bar detector occupancy. Uses the total occupancy at the stop bar presence detector combined with the count of vehicles served within the cycle (Smith 2014). • Directly measured using AVL data. This method can be used if the data are available at a high enough penetration rate. DETECTION NEEDS CALIBRATION REFERENCES • Advance detection is required for arrival and departure models but can also be used to detect queues extending to the advance detectors. • Stop bar presence detection is required for models that use occupancy and counts to estimate queues. N/A • Day, Bullock et al. (2014) • Liu et al. (2008) • Liu and Ma (2009) • Sharma, Bullock, and Bonneson (2007) • Smith (2014) PERFORMANCE MEASURE DETAILS 63

EXAMPLE USE During what times of day is an approach experiencing long queues? EXHIBIT 3-17 is a plot of the maximum queue lengths observed during individual cycles over a 24-hour period for a signalized approach in Indiana. The plot shows that during most of the day, queues are 300 feet or less. During the AM peak, however, queues can grow as long as 1,000 feet during certain cycles. If the approach is unable to accommodate 1,000 feet of queuing, signal timing adjustments may need to be investigated for the AM timing plan. EXHIBIT 3-17. ESTIMATED QUEUE LENGTH EXAMPLE: CHART OF QUEUE LENGTHS ON A SIGNALIZED APPROACH IN INDIANA (DAY, BULLOCK ET AL. 2014) USING METHOD PRESENTED BY LIU AND MA (2009) Queues Reach 1,000 feet During AM Peak Period 64 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.11 OVERSATURATION SEVERITY INDEX STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify oversaturated intersections and possible mitigations. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s PERFORMANCE MEASURE DETAILS 65

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES The temporal oversaturation severity index (TOSI) and spatial oversaturation severity index (SOSI) measure the impact of oversaturation and inform potential mitigations. • Temporal oversaturation. Refers to the impacts of split failures, when there is not sufficient green time to serve all the vehicles. Additional green time would have prevented this type of oversaturation. • Spatial oversaturation. Refers to the impact of downstream congestion, when queue spillbacks at other intersections prevent the movement of traffic. Adding green time in this case is detrimental. TOSI and SOSI are expressed as percentages. Both are ratios of the “unusable green time” and the “total available green time.” A high value of TOSI implies that additional green time is needed to avoid split failures. A high value of SOSI indicates that attention should be focused on the downstream intersection to create space for traffic to enter from the upstream intersection. TOSI = (L ÷ J × H) ÷ G SOSI = (∑Qi) ÷ G In these equations, the following are computed for individual cycles: L = Minimum residual queue length, feet J = Headway under congested traffic conditions, feet H = Saturation discharge headway, seconds G = Effective green time, seconds ∑Qi = Summation of all the durations when spillback blocked traffic, seconds (i.e., when there was a queue over an advance detector) Advance detection is required to identify queues. Additional details are available in the references. • Gettman, Abbas et al. (2012) • Gettman, Madrigal et al. (2012) • Wu, Liu, and Gettman (2010) 66 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE Did signal timing adjustments improve oversaturated conditions (i.e., downstream blockages and split failures)? EXHIBIT 3-18 summarizes SOSI (EXHIBIT 3-18A) and TOSI (EXHIBIT 3-18B) from a simulation study of oversaturated operations. The two charts multiply the indices by green time, effectively showing the amount of unusable green time due to spatial or temporal limitations. These values are reported by cycle throughout the simulation runtime for “before” and “after” conditions. The “after” data in each case shows a reduction in the unusable green time, indicating an improvement. Although spatial impacts are not completely eliminated in the after case (EXHIBIT 3-18A), they have been considerably improved, with only three cycles affected by downstream blockages. Temporal impacts (EXHIBIT 3-18B) were also addressed by the changes to the signal timing. EXHIBIT 3-18. OVERSATURATION SEVERITY INDEX EXAMPLE: (A) SPATIAL (SOSI) AND (B) TEMPORAL (TOSI) INDICES FROM A SIMULATION ENVIRONMENT (GETTMAN, MADRIGAL ET AL. 2012) Signal Timing Adjustments Improved Downstream Blockages Signal Timing Adjustments Improved Split Failures (A) (B) Simulation Time(s) Simulation Time(s) PERFORMANCE MEASURE DETAILS 67

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.12 PEDESTRIAN VOLUMES STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify time-of-day plan adjustments. • Identify intersections with high pedestrian volumes. • Identify pedestrian detection equipment that is malfunctioning. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 68 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION At locations with high pedestrian demand, it may be a priority to keep the cycle length low (to prevent delay) and maintain splits that are longer than the pedestrian clearance intervals (to prevent dropped coordination) during certain times of day. High pedestrian volumes can indicate that an agency should consider priority treatments such as leading pedestrian intervals or exclusive pedestrian phases. Planners may also be interested in pedestrian volumes as pedestrian facilities are being evaluated. Pedestrian volumes are often estimated using push button counts, but pedestrian-specific detectors can be installed for more accurate counts. The total number of pedestrians may be presented either as a raw count for a given time interval or during a given cycle, but volumes are typically expressed in units of pedestrians per hour, with the conversion being the same as for vehicle volumes (see Section 3.5: Vehicle Volumes). Similar to vehicle volumes, this metric can be used to identify detectors that are not working properly by comparing pedestrian counts to historical values. As pedestrian detection improves, it is likely that additional metrics will become available that will allow for enhanced pedestrian metrics (e.g., utilization of crossing time, compliance with signal indications). DETECTION NEEDS CALIBRATION REFERENCES Special detectors are needed that can identify pedestrian presence. These are not yet commonly used in practice but are available. N/A • UDOT EXAMPLE USE What is the pedestrian demand during different times of day? EXHIBIT 3-19 shows pedestrian volumes collected on a trail near a signalized intersection in Ogden, Utah. The data shows the distribution of pedestrian activity throughout the day, which illustrates the high number of pedestrians using the trail during the PM peak period. EXHIBIT 3-19. PEDESTRIAN VOLUMES EXAMPLE: PEDESTRIAN COUNT DATA FROM A TRAIL LOCATION (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Pedestrian Detector Located on Trail High Pedestrian Volumes During PM Peak PERFORMANCE MEASURE DETAILS 69

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.13 PEDESTRIAN PHASE ACTUATION AND SERVICE STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with high frequencies of pedestrian phase service. • Identify detection equipment that is malfunctioning. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 70 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Pedestrian actuations can serve as a proxy for pedestrian volumes at intersections without pedestrian-specific detection. At locations with low pedestrian actuations, a practitioner may decide to program vehicle splits that are less than the time required to serve the pedestrian clearance intervals (if allowed by the controller). While at locations with high pedestrian actuations, a practitioner may consider prioritizing pedestrians through features or modes such as Rest in Walk, leading pedestrian intervals (LPIs), or exclusive pedestrian phases. The level of detail examined can vary from microscopic to macroscopic: • At the microscopic level, individual button pushes can be measured or, alternatively, the earliest time in the cycle that a call for the pedestrian phase was received. • At the macroscopic level, the rate of pedestrian phase service can be aggregated over various time periods (e.g., hours, days). If a pedestrian phase is timing every cycle even during periods with low pedestrian volumes, it may be indicative of a faulty detector or a mis-programmed pedestrian recall. DETECTION NEEDS CALIBRATION REFERENCES Pedestrian push buttons are required. Locations without pedestrian detection will be using pedestrian recall or omitting pedestrian phases. N/A • Day et al. (2008) • Day, Premachandra, and Bullock (2011) • Day et al. (2014) • Day, Taylor et al. (2016) EXAMPLE USE 1 What times of day have high pedestrian actuations (and resulting pedestrian phase service)? EXHIBIT 3-20 shows pedestrian actuations relative to “time in cycle” for an intersection in Las Vegas, Nevada. The concept is similar to the Purdue Coordination Diagram (see Section 3.20: Purdue Coordination Diagram) except that the pedestrian phase times are shown rather than vehicle phase times and the dots represent actuations of the pedestrian push button. In this case, the chart is for a pedestrian crossing near the “Welcome to Fabulous Las Vegas” sign on Las Vegas Boulevard. The graph reveals that the busiest time for the attraction was between 10:00 AM and 6:00 PM. EXHIBIT 3-20. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: PEDESTRIAN PUSH BUTTON ACTUATIONS RELATIVE TO WALK TIMES (DAY, TAYLOR ET AL. 2016) End of Cycle Continuous Pedestrian Phase Service During the Middle of the Day Start of Walk Pedestrian Actuations Beginning of Cycle Detector Activation Change to Green Change to Yellow Change to Red PERFORMANCE MEASURE DETAILS 71

EXAMPLE USE 2 Will an exclusive pedestrian phase impact how often pedestrians request service (using the pedestrian push button)? EXHIBIT 3-21 is a chart of pedestrian phase service at a signalized intersection on a college campus under two different control schemes: (1) with conventional pedestrian phasing and (2) after implementation of an exclusive pedestrian phase. An exclusive pedestrian phase was initially considered for this location because many pedestrians were crossing against the signal. Implementation of the exclusive phase led to an increase in pedestrian phase utilization on every day of the week, meaning that pedestrians were more likely to request service (by pushing the button). Although this does not necessarily mean they crossed with the signal, it does mean that they were actuating it more often and, consequently, receiving more opportunities to cross safely. EXHIBIT 3-21. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: PERCENTAGE OF CYCLES WITH PEDESTRIAN PHASES BEFORE AND AFTER IMPLEMENTATION OF AN EXCLUSIVE PEDESTRIAN PHASE (DAY, PREMACHANDRA, AND BULLOCK 2011) Increase in Pedestrian Actuations After Implementation of an Exclusive Pedestrian Phase Conventional Pedestrian Phases Exclusive Pedestrian Phase 72 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE 3 Which locations (i.e., signalized intersections and corridors) have high rates of pedestrian activity? EXHIBIT 3-22 illustrates pedestrian actuations per day at signalized intersections in the Salt Lake City area. The total number of actuations is represented using a color scale, which can be used to identify “hot spots” of pedestrian activity. Although it is intuitive that the urban core has a high number of pedestrian actuations, there are some individual intersections and corridors in more peripheral areas that also exhibit high rates of pedestrian activity. EXHIBIT 3-22. PEDESTRIAN PHASE ACTUATION AND SERVICE EXAMPLE: NUMBER OF PEDESTRIAN CALLS PER DAY BY INTERSECTION IN THE SALT LAKE CITY AREA (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) Urban Core Has a High Number of Pedestrian Actuations Per Day Other Peripheral “Hot Spots” of Pedestrian Activity Are Located Outside of the Urban Core <50 50–90 100–199 200–299 300–399 400–499 500+ Unknown Average Daily Pedestrian Actuations PERFORMANCE MEASURE DETAILS 73

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.14 ESTIMATED PEDESTRIAN DELAY STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify phases/intersections with high pedestrian delay. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 74 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION If there are certain times of day or certain intersections with high levels of pedestrian delay, an agency can consider implementing measures that prioritize pedestrians. These can include pedestrian-specific treatments such as Rest in Walk, leading pedestrian intervals (LPIs), or exclusive pedestrian phases, but signal timing adjustments may be required beyond pedestrian phasing and clearance settings. A long pedestrian delay may be the result of long cycle lengths, long split times on conflicting phases, or phase order. Planners may also be interested in pedestrian delay, particularly for comparison to vehicle and bicycle delay. This metric reports the time per cycle between the earliest call for a pedestrian phase (from a button push) until the beginning of the next Walk interval. (Pedestrian Delay) = (Start of Walk Interval Time) – (First Button Push Time) Although the metric does not necessarily reflect the actual time at which the pedestrian executed the crossing movement, it does reveal the amount of time elapsed between the request and when the controller provided the requested pedestrian interval. In the future, passive pedestrian detection might be used to better estimate delay. DETECTION NEEDS CALIBRATION REFERENCES N/A • Hubbard, Bullock, and Day (2008) EXAMPLE USE 1 Are there times of day when pedestrians are experiencing long delays? EXHIBIT 3-23 shows pedestrian actuations and delays for a signalized intersection on the edge of the Texas A&M campus in College Station, Texas. The chart shows a high rate of pedestrian demand from 7:00 AM through 12:00 AM. Such information can be used to demonstrate the need for potential engineering treatments such as changing left turns to protected-only, adding “No Turn on Red” signs, or implementing exclusive pedestrian phases. EXHIBIT 3-23. PEDESTRIAN DELAY EXAMPLE: PEDESTRIAN DELAY FOR A SIGNALIZED INTERSECTION WITH A HIGH NUMBER OF PEDESTRIAN CALLS (COURTESY COLLEGE STATION, TEXAS) Note: Times are displayed in GMT; therefore, 2:00 AM is 7:00 AM (Central Time) and 7:00 PM is 12:00 AM. Delay Between Button Push and Walk Interval High Pedestrian Activity All Day with Delay Consistently Above 1 Minute Pedestrian Push Button Activated Pedestrian Delay by Actuation Pedestrian push buttons are required. Locations without pedestrian detection will be using pedestrian recall or omitting pedestrian phases. PERFORMANCE MEASURE DETAILS 75

EXAMPLE USE 2 What level of service do pedestrians experience at a signalized intersection? EXHIBIT 3-24 is a chart of pedestrian delay at a fully actuated, non-coordinated traffic signal. The delay values have been sorted from largest to smallest and then categorized by Highway Capacity Manual (HCM) level of service (LOS) values. The wide range of cycle lengths at the fully actuated signal led to a wide range of pedestrian delay values (some in excess of 2 minutes). Slightly more than half of the cycles had LOS E or better (60 seconds of delay or less). EXHIBIT 3-24. PEDESTRIAN DELAY EXAMPLE: PEDESTRIAN DELAY AT A FULLY ACTUATED, NON-COORDINATED SIGNAL (HUBBARD, BULLOCK, AND DAY 2008) Pedestrians Experience LOS E or Better During Slightly More than Half of the Cycles 76 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.15 ESTIMATED PEDESTRIAN CONFLICTS STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with a high number of potential conflicts between vehicles and pedestrians. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s PERFORMANCE MEASURE DETAILS 77

Detectors past the stop bar are required for accurate identification of turning vehicles that conflict with the pedestrian crossings. These detection zones should be small (and lane-by-lane if possible) so they can accurately capture individual vehicles. It is also useful, though not required, to have pedestrian detection so that cycles with pedestrians present can be identified. N/A • Hubbard, Bullock, and Day (2008) EXAMPLE USE What are the highest conflicting vehicular flow rates across pedestrian crossings? EXHIBIT 3-25 compares right-turn vehicular flow rates during cycles with pedestrian activity for 2 different days on a college campus. August 13, 2007 (blue) was 1 week prior to the start of classes, and August 22, 2007 (red) was during the first week of classes. The data series are sorted from largest to smallest flow rate. The increase in right-turn volume after classes began is evident with more cycles having higher flow rates, particularly the 15 cycles with the highest rates of the day. It is not uncommon to have pedestrian intervals with conflicting volumes of 1,200 vehicles/hour, which corresponds to an average headway of 3 seconds between vehicles. This particular conflict grew severe enough that an exclusive pedestrian phase was eventually implemented. EXHIBIT 3-25. PEDESTRIAN CONFLICTS EXAMPLE: RIGHT-TURN VEHICULAR FLOW RATES DURING CYCLES WITH PEDESTRIAN ACTUATIONS (HUBBARD, BULLOCK, AND DAY 2008) 15 Cycles with the Highest Vehicular Flow Rates 1,200 Vehicles/Hour Corresponds to 3-Second Average Headways for Vehicles Turning Right Across a Pedestrian Movement DESCRIPTION Pedestrians typically cross at the same time vehicles are turning, commonly including both permitted left-turn and right-turn movements. This metric reports the amount of conflicting volume, which serves as an estimate for the degree of “pressure” potentially placed on the pedestrians by the vehicles passing through the same space. The higher the number, the greater the potential for conflicts. For pedestrian crossings with a high number of potential conflicts, pedestrian-specific treatments such as Rest in Walk, leading pedestrian intervals (LPIs), or exclusive pedestrian phases may increase pedestrian safety and comfort. Alternatively, vehicle movements can be restricted by changing left turns to protected-only or adding “No Turn on Red” signs. DETECTION NEEDS CALIBRATION REFERENCES August 13, 2007 August 22, 2007 78 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.16 YELLOW/RED ACTUATIONS STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with high numbers of red-light-running vehicles and/or severe violations. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s PERFORMANCE MEASURE DETAILS 79

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Red light running is a safety concern. If vehicles are frequently running the red light, an agency should consider countermeasures that will reduce the likelihood of vehicles entering the intersection on red. This metric reports actuations (on detectors located either at or past the stop bar) relative to the phase state: yellow, red clearance, or red (after the beginning of the next phase). The actuation times can be used to estimate the number of vehicles running the red light and the amount of time into the red that the events occurred, with the most severe being those that happen after the start of green and start-up lost time for the next phase. A large amount of violations is an indicator that the intersection may benefit from a safety evaluation. There are several potential causes that can be investigated: • Green may be too short, causing drivers to push into yellow/red. Split adjustments might be needed. • Coordination may be poor. A large number of arrivals on red or truncation of a platoon by the end of green may be an issue. Detectors at or past the stop bar are required for accurate identification of vehicles entering the intersection on yellow and/or red. These detection zones should be small, so they can accurately capture individual vehicles. Some detection technologies allow actuations to only be recorded if vehicles are traveling over a specified speed. This is beneficial for detection located at the stop bar because slow-moving vehicles coming to a stop can be omitted. N/A • Mackey (2017) • Taylor (2016) • UDOT • Detection locations and settings may result in vehicles getting caught in the decision zone (having to decide whether to stop or go). Adjusting the detection zones and passage settings can reduce the number of drivers having to make the go/no-go decision. • Sight distance may be poor; some drivers might not be able to see the indication due to obstructions. • Law enforcement may be needed. If deployed, this metric can help identify specific times of day for additional enforcement, as well as provide a means to evaluate impacts. Note that some of the above items can be investigated using other performance measures, including a review of vehicle delay and progression quality (see Section 3.9: Estimated Vehicle Delay and 3.19: Progression Quality). Other items may require a field visit, such as sight distance issues. 80 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE Are there times of day with high numbers of vehicles running the red light? EXHIBIT 3-26 shows 24 hours of yellow and red actuations for a phase. In this case, there are severe violations in the AM, with vehicles entering the intersection well into the service time for the next phase. EXHIBIT 3-26. YELLOW/RED ACTUATIONS EXAMPLE: 24-HOUR YELLOW/RED ACTUATIONS (TAYLOR 2016) Severe Red-Light-Running Violations During the AM Peak Red Red Clearance Yellow Clearance Detector Activation PERFORMANCE MEASURE DETAILS 81

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.17 RED-LIGHT- RUNNING (RLR) OCCURRENCES STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with high numbers of red-light-running vehicles and/or severe violations. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 82 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

RLR occurrences are determined based on maximum values of tarr and ton (to avoid counting RTOR and left-turn clipping). A field study found values of ton = 2.0 seconds and tarr = 5.0 seconds to be reasonable, but these will need to be calibrated at other locations. Requires stop bar presence detection. Lane-by-lane detection provides more accurate results than detectors tied together across lanes; multi-lane detectors may over-estimate occupancy ratios. DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Red-light-running (RLR) vehicles can be detected by analyzing stop bar detector occupancy relative to the phase state. Although similar to yellow/red actuations (see Section 3.16: Yellow/Red Actuations), this metric uses stop bar presence detection to determine RLR. It logs RLR occurrences when a stop bar detector becomes occupied shortly after the beginning of red and then becomes unoccupied shortly later in the red interval (shown in EXHIBIT 3-27). The arrival of the vehicle (detector “on” time) is marked by tarr, and the duration of the subsequent occupied interval is ton. Long values of ton are more likely to be right turns on red (RTOR) than RLR, while very high values of tarr are likely caused by left-turning vehicles on the cross street clipping the stop bar detection zone. When the number of RLR occurrences is high, it may warrant a safety evaluation at the intersection. Although not all fixes will be related to signal timing (e.g., changes to geometry), the splits, offsets, and detection locations and associated settings can play a role. • Lavrenz et al. (2016) EXHIBIT 3-27. RED-LIGHT-RUNNING OCCURRENCE CALCULATION USING STOP BAR DETECTOR OCCUPANCY PERFORMANCE MEASURE DETAILS 83

EXAMPLE USE Did a split increase result in a reduced number of red-light-running vehicles? EXHIBIT 3-28 shows RLR as a daily count for a single phase over a 5-month period. Halfway through the study period, the split was increased, which correlates to a decrease in the number of RLR vehicles. EXHIBIT 3-28. RED-LIGHT-RUNNING (RLR) OCCURRENCES EXAMPLE: COUNT OF RLR BEFORE AND AFTER SPLIT ADJUSTMENT (LAVRENZ ET AL. 2016) Number of Red-Light- Running Vehicles Decreased After Split Increase Split Increase 84 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.18 EFFECTIVE CYCLE LENGTH DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Confirm coordinated plans are operating as intended. • Confirm how adaptive systems are adjusting effective cycle lengths. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE PERFORMANCE MEASURE DETAILS 85

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Effective cycle length is the amount of time used to serve all the phases at an intersection. Each phase will usually have an opportunity for service within a cycle, unless it is skipped (or omitted) by the signal controller. Effective cycle length can be measured at most intersections using one of the following: • Comparing subsequent times of “barrier crossings” (e.g., between Phases 1, 2, 5, and 6 and Phases 3, 4, 7, and 8 in conventional eight-phase control). • Comparing subsequent ends of green for coordinated phases, if termination of those phases is needed to serve conflicting movements. In cases with complex phasing structures, site-specific definitions of cycle length might be needed. Note that times between subsequent yield points or other repeating coordinated times offer a way to measure the background cycle length. However, the effective cycle length (influenced by actuations) would be unknown. In addition, those coordinated points are unavailable during free operations. Effective cycle length can be used to confirm coordinated plans are operating as intended and is also useful for advanced applications. For example, it can be used to assess how often and by how much an adaptive system is changing the cycle length. Although high-resolution data from the controller can report this metric directly (without programming detection settings), it will be most meaningful at actuated intersections. Without detection, cycle lengths will remain constant. The analyst must in some cases select an appropriate method for measuring cycle length. • Day et al. (2008) • Richardson et al. (2017) 86 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE What are the seasonal impacts on effective cycle length for a corridor utilizing an adaptive system? EXHIBIT 3-29 shows cycle lengths over several months for a corridor in Utah that uses an adaptive cycle length algorithm. The chart demonstrates the seasonal variation in cycle lengths due to seasonal changes in traffic volumes. During much of April and May, the maximum cycle length of 110 seconds was reached almost every day. However, in July and August, there were many days when the cycle length did not climb much higher than its minimum value. In September, the cycle lengths began to increase again. EXHIBIT 3-29. EFFECTIVE CYCLE LENGTH EXAMPLE: EFFECTIVE CYCLE LENGTHS USING AN ADAPTIVE SYSTEM DURING A 6-MONTH PERIOD (RICHARDSON ET AL. 2017) Lower Volumes During Summer Months Resulted in Adaptive System Selecting Lower Cycle Lengths Cycle Lengths Observed PERFORMANCE MEASURE DETAILS 87

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.19 PROGRESSION QUALITY STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections/corridors with poor progression (i.e., low POG, platoon ratios, or arrival types). 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 88 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Advance detection upstream of a signalized approach is used to estimate vehicle arrivals at the stop bar. Detectors located at the beginning of the decision zone (approximately 5 seconds of travel time from the stop bar) are often available, but detectors used for this metric should be placed to avoid queuing over the advance detector. DESCRIPTION A common reason for coordinating signalized intersections is progressing traffic platoons to reduce stops, delay, and travel time along a particular route. There are several ways to report progression quality: percent on green (POG), platoon ratio, and arrival type. Percent on green. Reports the number of vehicles that arrive on green. An advance detector is required to compute this metric. Each vehicle actuation is associated with a time of arrival at the intersection, which is then compared to the signal state (red or green). (Percent on Green) = (Arrivals on Green) ÷ (Total Arrivals) Platoon ratio. A modification of POG that accounts for long green times as follows: METRIC VALUE PROGRESSION QUALITY 0.33 1 Very poor 0.67 2 Unfavorable 1.00 3 Random arrivals 1.33 4 Favorable 1.67 5 Highly favorable 2.00 6 Exceptionally favorable EXHIBIT 3-30. RELATIONSHIP BETWEEN PLATOON RATIO AND ARRIVAL TYPE (HCM 6TH EDITION) DETECTION NEEDS (Platoon Ratio) = (POG) ÷ (g/C) where g/C = “Green-to-cycle length” ratio. This value is computed by dividing the green time, g, by the effective cycle length, C, of the cycle in which the green interval occurred. “Arrival type.” A Highway Capacity Manual (HCM) metric that divides platoon ratios into a score of 1–6, with 1 representing poor progression and 6 representing excellent progression. EXHIBIT 3-30 explains how to convert a platoon ratio into an arrival type. Each of these metrics can be used to assess progression quality either at an intersection or along a corridor. The more vehicles arriving on green, the better the progression quality. PERFORMANCE MEASURE DETAILS 89

CALIBRATION REFERENCES An adjustment is made to the vehicle detection times to estimate the time each vehicle arrives at the stop bar. This can be done by adding the travel time to the detection time. For example, if the detector is positioned 5 seconds upstream of the stop bar, 5 seconds would be added to the reported actuation times. The actual red and green intervals at the intersection would typically be converted into “effective red” and “effective green” times. This is discussed in more detail in Section 3.8: Split Failures. • Day et al. (2008) • Day and Bullock (2010) • Day et al. (2014) • Day, Bullock et al. (2016) • Mackey (2017) • Smaglik, Bullock, and Sharma (2007) • Smaglik, Sharma et al. (2007) EXAMPLE USE 1 Are any intersections along a corridor experiencing lower progression quality? EXHIBIT 3-31 is a plot of POG for three intersections along a corridor. Each day, the average POG is reported for the period between 6:00 AM and 10:00 PM. Weekend values tend to be lower than weekday values because the corridor is only coordinated on weekdays. This chart can be used to identify changes in progression quality. In this example, the northbound approach at Intersection #6 Airport experiences a sudden decrease in POG in late November. This indicates an offset adjustment should be investigated. This change may be the result of a permanent change in traffic patterns or a temporary condition such as construction. EXHIBIT 3-31. PROGRESSION QUALITY EXAMPLE: PERCENT ON GREEN (POG) OVER A 1-MONTH PERIOD (DAY, BULLOCK ET AL. 2016) Decrease in POG for Northbound Approach at Intersection #6 Intersections are Uncoordinated on Weekends Resulting in Lower POG 90 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Did offset adjustments increase or decrease progression quality along a corridor? EXHIBIT 3-32 summarizes POG before and after offset adjustments on a corridor in Utah. Each pie chart shows the existing POG as a light green series; the dark green series represents any increases in POG that occurred on each approach, and the red series represents decreases. Ideally, the change would yield mostly increases, which is the case in this situation. There are five approaches with increases in POG above 10%. Five approaches experienced POG decreases, but the decreases were between 1–3%. EXHIBIT 3-32. PROGRESSION QUALITY EXAMPLE: OFFSET ADJUSTMENT IMPACT ON PERCENT ON GREEN (POG) (MACKEY 2017) Offset Adjustment Increased Percentage of Southbound Vehicles Arriving on Green at 1300 S Southbound POG Northbound POG Offset Adjustment Decreased Percentage of Northbound Vehicles Arriving on Green at 2100 E Initial Percent Arrival on Green Increase in Percent Arrival on Green Decrease in Percent Arrival on Green EXAMPLE USE 2 PERFORMANCE MEASURE DETAILS 91

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.20 PURDUE COORDINATION DIAGRAM STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify when vehicles are arriving during the cycle (i.e., on green or red) for a particular phase or overlap at an intersection. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 92 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION While similar to Progression Quality metrics (see Section 3.19: Progression Quality), the Purdue Coordination Diagram (PCD) provides additional detail on vehicle arrivals during the cycle (e.g., near the beginning of the cycle or end of the cycle). Percent on green, platoon ratio, and arrival type can help identify locations that would benefit from coordination adjustments (i.e., to cycle lengths, splits, offsets, and phase order), and the PCD can help identify the values that should be chosen for those adjustments. Additionally, the PCD can be used to monitor general intersection operations. Because the basis for the diagram is time in cycle, it can be useful for monitoring advanced applications such as traffic responsive or adaptive control. The PCD is a graphical representation of individual vehicle arrivals at the stop bar relative to the signal state (green, yellow, or red), as illustrated in EXHIBIT 3-33. Each diagram depicts vehicle arrivals for one phase (or overlap) at one signalized intersection. They are most often used to depict multiple cycles (e.g., over 24 hours or several days), but can also be used to display EXHIBIT 3-33. PURDUE COORDINATION DIAGRAM EXPLANATION (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) data for a single cycle. For example, the top half of EXHIBIT 3-33 shows a full day of data, while the bottom half of EXHIBIT 3-33 shows a zoomed view of seven cycles that occurred over a 15-minute period on that same day. The basic components of the PCD are three data series: (1) the beginning of green in each cycle, (2) the end of green in each cycle, and (3) the vehicle arrival times. Additional information can be overlaid such as pattern changes, approach volumes, the yellow interval, and percent arrivals on green. The time in cycle is the basis for each individual piece of data displayed in the chart. In the PCD, this is calculated as the time since the previous beginning of red time. When the cycle length is reached (at the beginning of red), a new cycle begins at 0 (zero) seconds. A constant cycle length is not required to display the PCD. For example, EXHIBIT 3-33 displays a fully actuated period during the middle of the night when no programmed cycle length is in effect. Beginning of Red Beginning of Green Vehicles Arriving on Red Vehicles Arriving on Green Beginning of Red Beginning of Green Vehicles Arriving on Red Vehicles Arriving on Green Detector Activation Change to Green Change to Yellow Change to Red PERFORMANCE MEASURE DETAILS 93

DETECTION NEEDS CALIBRATION REFERENCES Advance detection upstream of a signalized approach is used to estimate vehicle arrivals at the stop bar. Detectors located at the beginning of the decision zone (approximately 5 seconds of travel time from the stop bar) are often available, but detectors used for this metric should be placed to avoid queuing over the advance detector. An adjustment is made to the vehicle detection times to estimate the time each vehicle arrives at the stop bar. This can be done by adding the travel time to the detection time. For example, if the detector is positioned 5 seconds upstream of the stop bar, 5 seconds would be added to the reported actuation times. The actual red and green intervals at the intersection would typically be converted into “effective red” and “effective green” times. This is discussed in more detail in Section 3.8: Split Failures. • Brennan et al. (2011) • Day and Bullock (2009) • Day and Bullock (2010) • Day et al. (2010) • Day et al. (2014) • Day, Bullock et al. (2016) 94 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE Did offset adjustments improve progression for a particular approach at an intersection? The Virginia Department of Transportation adjusted offsets along a corridor near Charlottesville, Virginia. EXHIBIT 3-34A is a PCD from before the offset adjustments. Southbound vehicle arrivals at this particular intersection were arriving prior to the green interval during the midday timing plan. After the offset adjustments, a higher percentage of vehicles arrived on green, as shown in EXHIBIT 3-34B. A PCD chart can be used to validate that a signal timing change improved progression or, alternatively, if an additional adjustment is required. EXHIBIT 3-34. PURDUE COORDINATION DIAGRAM EXAMPLE: OFFSET ADJUSTMENT (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Detector Activation Change to Green Change to Yellow Change to Red Detector Activation Change to Green Change to Yellow Change to Red (A) Before offset adjustment (B) After offset adjustment Vehicles Arriving on Red During Midday Offset Adjustment Resulted in Vehicles Arriving on Green During Midday PERFORMANCE MEASURE DETAILS 95

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.21 CYCLIC FLOW PROFILE STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections/corridors with poor progression. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 96 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION This metric reports the distribution of green time and vehicle arrivals during an “average” cycle over some time period. The information is similar to that in the Purdue Coordination Diagram (PCD, Section 3.20: Purdue Coordination Diagram) except that it is aggregated over a time period rather than for individual cycles. One advantage of this metric is being able to examine progression at many intersections at once. Although PCDs can also be used for this purpose, it can become difficult to evaluate the data if too many are presented in a single graphic. Cyclic flow profiles provide an effective summary format for determining if the highest probability of vehicle arrivals aligns with the highest probability of green. For each cyclic flow profile, the same background cycle length (pattern) should be in effect for the chosen time period. The method of calculation relies on a time in cycle that is different from that used in other metrics. The PCD uses the last beginning of red. However, cyclic flow profiles use the time in the system cycle, which is referenced from a master clock zero point. No specific red and green events are used to define the cycle. During coordination, the system cycle exists in the background of every coordinated controller; the current time in the system cycle is equal to the number of seconds since a reference time (usually midnight), modulo C, where C is the cycle length: (Time in System Cycle) = (Seconds after Reference Time) mod C For example, consider the timestamp 09:08:16. If the reference time is 00:00:00 (midnight), and the cycle length is 100 seconds, the time in the system cycle is: [(9 × 3,600) + (8 × 60) + (16)] mod 100 = 32,896 mod 100 = 96 This time is used to assemble the green and vehicle arrival profiles, as illustrated in EXHIBIT 3-35. • For the probability of green, the state of green should be known for every 1 second interval within the time period. For each second, it is possible to look up the most recent red or green event and use that to deduce whether the signal was red or green. This yields a “matrix” of green status for each cycle in the chosen time period and time within that cycle. The probability of green can be calculated by summing all of the green events for a particular time in the cycle. • The arrival profile is found similarly. Rather than looking up the status of green, the number of vehicles detected within every 1 second interval is known. The value for the cyclic flow profile is found by summing the number of vehicles detected at a particular time in cycle across all the cycles. The final flow profile graphic is created by combining the two pieces of information into one chart. One option would be to represent the arrivals as bars on top of the distribution of green, as shown in EXHIBIT 3-35. PERFORMANCE MEASURE DETAILS 97

DETECTION NEEDS CALIBRATION REFERENCES Advance detection upstream of a signalized approach is used to estimate vehicle arrivals at the stop bar. Detectors located at the beginning of the decision zone (approximately 5 seconds of travel time from the stop bar) are often available, but detectors used for this metric should be placed to avoid queuing over the advance detector. An adjustment is made to the vehicle detection times to estimate the time each vehicle arrives at the stop bar. This can be done by adding the travel time to the detection time. For example, if the detector is positioned 5 seconds upstream of the stop bar, 5 seconds would be added to the reported actuation times. The actual red and green intervals at the intersection would typically be converted into “effective red” and “effective green” times. This is discussed in more detail in Section 3.8: Split Failures. • Day and Bullock (2010) • Day and Bullock (2011) • Day, Brennan, Hainen et al. (2011) • Day and Bullock (2014) • Robertson (1969) • Shelby et al. (2007) Exhibit 3-35. CYCLiC FLOW PROFiLE COMPUtAtiON ExPLANAtiON (DAY AND bULLOCK 2011) Green Red 0 1 2 98 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE How much and at which locations did offset adjustments improve progression along a corridor? EXHIBIT 3-36 shows cyclic flow profiles for an eight-intersection corridor with each intersection having its own flow profile. For simplicity, the axes are removed from the profiles and only the graphics are shown. The sixteen profiles in EXHIBIT 3-36A show conditions before changes were made to offsets while those in EXHIBIT 3-36B show conditions afterward. Before offset adjustments, there were several approaches where most of the vehicle arrived during red or too early or too late in the green to be progressed. After offsets were adjusted, platoons at four of the five approaches arrived during the green, indicating improved progression. Progression at the southbound approach at Intersection 3 did not change; however, this is likely because any changes to the offset would have worsened conditions in the opposite direction. It is not always possible to achieve progression at every approach in both directions. EXHIBIT 3-36. CYCLIC FLOW PROFILE EXAMPLE: CORRIDOR APPLICATION (DAY AND BULLOCK 2011) (A) Before offset adjustments (B) After offset adjustments Before Offset Adjustments, Approaches at Intersections 3, 4, 5, and 6 Experienced Most Vehicles Arriving on Red After Offset Adjustments, More Vehicles Arrive During Green at Intersections 4, 5, and 6 PERFORMANCE MEASURE DETAILS 99

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.22 OFFSET ADJUSTMENT DIAGRAM STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify corridors with potential for progression improvement from offset adjustments. • Estimate impact of proposed offset adjustments. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 100 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION This metric reports potential progression quality for individual coordinated approaches along a corridor. If calculated for approaches along several corridors, it can be used to quickly determine locations where an agency will have the greatest return on investment when adjusting offsets. This metric is based on the Cyclic Flow Profile (Section 3.21: Cyclic Flow Profile). Potential progression quality of an individual approach is determined by comparing the green and vehicle arrival distributions. If the cyclic positions are shifted relative to each other by means of an offset adjustment, then the arrivals on green will change. It is possible to assess all the adjustments by shifting one of the distributions through the entire cycle and recording the performance in each case. EXHIBIT 3-37 illustrates how the resulting sinusoidal offset performance EXHIBIT 3-37. OFFSET ADJUSTMENT DIAGRAM EXPLANATION (DAY AND BULLOCK 2017) curve can be “flattened” into a bar that describes the minimum and maximum values. The current value is plotted on top of this range. For example, in EXHIBIT 3-37, the current percentage of vehicles arriving on green for this approach is 48%, but with certain offset combinations it can potentially be as high as 81% or as low as 12%. Data for multiple approaches can be combined into a composite plot with columns representing different approaches along a corridor (see example use). It is also possible to add the predicted progression quality resulting from a set of trial offset adjustments to such a plot. A simple, effective model for predicting optimal offset adjustments has been described in several previous studies (Day and Bullock 2011; Day and Bullock 2017). Offset Adjustment(s) PERFORMANCE MEASURE DETAILS 101

CALIBRATION REFERENCES An adjustment is made to the vehicle detection times to estimate the time each vehicle arrives at the stop bar. This can be done by adding the travel time to the detection time. For example, if the detector is positioned 5 seconds upstream of the stop bar, 5 seconds would be added to the reported detection actuation times. The actual red and green intervals at the intersection would typically be converted into “effective red” and “effective green” times. This is discussed in more detail in Section 3.8: Split Failures. • Day and Bullock (2011) • Day, Bullock et al. (2016) • Day and Bullock (2017) DETECTION NEEDS Advance detection upstream of a signalized approach is used to estimate vehicle arrivals at the stop bar. Detectors located at the beginning of the decision zone (approximately 5 seconds of travel time from the stop bar) are often available, but detectors used for this metric should be placed to avoid queuing over the advance detector. 102 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE What is the potential for progression improvement along a coordinated corridor? EXHIBIT 3-38 is an example Offset Adjustment Diagram for a corridor with five intersections: 10 approaches labeled eastbound (eb) and westbound (wb). Each blue bar represents the range of potential percent on green (POG) for each individual approach (see Section 3.19: Progression Quality). The black dot represents the POG before an offset adjustment. The green diamond represents the predicted value of POG using the offset adjustments shown at the top of the chart. A green line between the “before” value and the predicted value indicates a predicted improvement in POG, whereas a black line illustrates a predicted reduction in POG. This chart also shows the “after” values of POG, obtained from a different day after the new offsets were implemented. As an example, the westbound approach at Intersection (Int.) 3 had a “before” POG of 35%. An analysis of the vehicle arrival and green distributions identified that possible POG values ranged between 28% and 65%. After offset optimization, it was predicted that the POG would increase to 60%. The “after” data shows that the actual performance was slightly better than the predicted performance. This type of chart can be used to identify locations where POG can likely be improved because the existing value is low compared to the possible POG values. Alternatively, if most intersections have POG values that are high compared to the possible values, an agency could decide to use resources elsewhere. If these types of charts were developed for multiple corridors, they could be used to prioritize corridors for offset adjustments. EXHIBIT 3-38. EXAMPLE OFFSET ADJUSTMENT DIAGRAM: PERCENT ON GREEN (POG) ASSESSMENT FOR FIVE-INTERSECTION CORRIDOR (DAY AND BULLOCK 2017) Low Probability of POG Improvement for Int. 2 Eastbound Approach Because Existing POG is at Maximum Predicted Value High Potential for POG Improvement at Int. 3 Westbound Approach Because Existing POG is Low Compared to Predicted Value Before Predicted After Predicted Improvement Predicted Degradation Possible Values PERFORMANCE MEASURE DETAILS 103

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.23 TRAVEL TIME AND AVERAGE SPEED STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify corridors with high/low travel times and speeds. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 104 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

Some detection may be required depending on the type of technology being used to track vehicles. DESCRIPTION Travel time and speed are metrics that can be shared with decision-makers and the public to effectively communicate signal timing impacts on vehicles, pedestrians, bicycles, and other modes of travel. They can also be used by agencies to prioritize signal timing activities; agencies can normalize the data and identify outliers where travel times and speeds are higher or lower than historical values. Travel time is defined as the amount of time needed to traverse a route (the inverse of which is average speed). There are numerous ways to measure or estimate travel time using the following data sources: • AVL data. The time spent by vehicles along a route can be deduced after map- matching the GPS positions to distances along a roadway. Travel times can be directly measured based on the path of the probe vehicle. • AVI data. Travel times from point to point can be measured using the difference in time between detection at two locations. Typically, sensors are located such that there is a single route between them. • Segment speed data. The average speed on individual segments that make up a route can be converted into travel times, with the total travel time on the route summed from that of the individual segments. • High-resolution data. Delays on movements at individual intersections may be estimated and added together to estimate travel time. Another method is called the “virtual probe vehicle” technique, which estimates the trajectory of a virtual probe vehicle based on queue lengths at each intersection. Some of the methods discussed give the travel times of individual vehicles, whereas others yield a representative travel time for a particular time period. The data can be visualized in numerous ways, including: • Raw data shown in a point cloud. • Aggregated data shown as a linear series over time (e.g., 5 minute averages over the course of a day). » Speeds aggregated over a time period can be shown as profiles, which can depict the amount of time that different speed categories were observed. » Travel times aggregated over a time period can be shown as histograms or cumulative frequency diagrams (which are useful for comparing two different time periods). • Single-value numerical measures can be derived from travel time and speed data to offer a summary of performance. For example, the “planning time index” represents the 95th percentile travel time divided by the free-flow travel time. DETECTION NEEDS CALIBRATION A reference travel time can be helpful to give context to measured travel times. The desired or free-flow speed (perhaps approximated using speed limits) can be used for this comparison. PERFORMANCE MEASURE DETAILS 105

REFERENCES • Day et al. (2010) • Day, Young et al. (2017) • Hu, Fontaine, and Ma (2016) • Krohn et al. (2017) • Lavrenz et al. (2016) • Liu et al. (2008) • Mathew et al. (2017) • Quayle et al. (2010) • Sharifi et al. (2016) • Talukder et al. (2017) • Wasson, Sturdevant, and Bullock (2008) • Young et al. (2017) EXAMPLE USE 1 Did offset adjustments impact corridor speeds? EXHIBIT 3-39 shows average, 5th percentile, and 95th percentile speeds for a corridor near Charlottesville, Virginia, (A) before and (B) after offset adjustments. The Virginia Department of Transportation made offset adjustments along the corridor during the midday because it was experiencing lower progression quality than during other times of day. Offset adjustments were made in June, and resulted in higher progression quality along the corridor and speeds that better aligned with other times of day. EXHIBIT 3-39. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: IMPACT OF OFFSET ADJUSTMENTS ON SPEED (COURTESY VIRGINIA DEPARTMENT OF TRANSPORTATION) Midday Speeds are Aligned with Other Times of Day After Offset Adjustments (B) After offset adjustments Midday Speeds Were Lower than Other Times of Day Before Offset Adjustments (A) Before offset adjustments 106 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE 2 Where are the most critical intersections based on travel times and reliability? EXHIBIT 3-40 is an example of aggregated travel time data. Values are shown for 39 corridor directional pairs in the greater Indianapolis area (for a total of 78 data points). The travel times are shown for 1 week of operation for the 6:00 AM to 9:00 AM period. The data are plotted according to the median travel time along the horizontal axis and the interquartile range (IQR) of the travel time along the vertical axis. IQR is the difference between the 75th and 25th percentiles. Both axes are normalized according to the speed limit travel time, so that data from corridors with different speed limits can be compared together. The red line at 100% indicates where median travel times are higher than the speed limit travel time. In other words, points to the left of this line have average speeds greater than the speed limit, and points to the right have average speeds lower than the speed limit. Most of the corridors have longer travel times than the speed limit travel time. As the median travel times increase, the IQR of the travel times also generally increase, meaning that travel times become less reliable. From such a chart, it becomes relatively easy to identify corridors that stand out from the others as candidates for further investigation. This methodology can be used on a district- or agency-wide level as a screening tool for deciding where to potentially make investments in signal operation. EXHIBIT 3-40. TRAVEL TIME AND AVERAGE SPEED EXAMPLE: CORRIDOR RANKING USING TRAVEL TIME DATA (MATHEW ET AL. 2017) Lower Travel Times Lower Reliability Higher Reliability CRITICAL LOCATIONS Higher Travel Times PERFORMANCE MEASURE DETAILS 107

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.24 TIME-SPACE DIAGRAM STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify corridors with small green bands (i.e., poor opportunities for progression). 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 108 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION The Time-Space Diagram is a classic representation of signal coordination and a basic design tool for creating timing plans. The y-axis represents space (linear distance along a corridor) and the x-axis represents time. The signal state is represented by ring-and-barrier diagrams showing the green and red indications of the coordinated phases. Across the signal state, individual vehicle trajectories can be represented as lines. “Green bands” (opportunities for progression) can be determined by drawing diagonal lines between the green intervals of neighboring intersections. The slope of vehicle trajectories or green bands represents speed, which can be the speed limit or some other running speed intended for progression. Additional information is available in NCHRP Report 812: Signal Timing Manual, 2nd ed. (Urbanik et al. 2015). Such diagrams are often prepared in software when designing signal timing plans for implementation in the field. Actual operations may differ, in particular because of actuation. It is possible for the signal controller to record green and red times to draw a diagram of actual operations (which some central systems have been capable of for many years). DETECTION NEEDS CALIBRATION REFERENCES None. However, AVL detection is required for overlaying vehicle trajectories. Speeds are needed to draw bands in Time-Space Diagram. • Liu et al. (2008) • Urbanik et al. (2015) EXAMPLE USE Is the signal timing resulting in expected green bands? EXHIBIT 3-41 is a Time-Space Diagram from the Clark County, Washington, ATMS system. In addition to the red and green times, the effective arterial through bands are shown. The system provides consistent eastbound (blue) bands; westbound (red) bands are not as frequent and typically are smaller. This graphic can be used to confirm that the system is operating as intended. EXHIBIT 3-41. TIME-SPACE DIAGRAM EXAMPLE: TIME-SPACE DIAGRAM FROM THE CLARK COUNTY, WASHINGTON, ATMS SYSTEM SHOWING ACTUAL GREEN BANDS (COURTESY CLARK COUNTY, WASHINGTON) Eastbound Green Bands Are Typically Larger Westbound Green Bands Are Typically Smaller PERFORMANCE MEASURE DETAILS 109

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.25 PREEMPTION DETAILS STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with high numbers of preemption events. • Identify intersections with preemption events causing high delay for other transportation system users. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s 110 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

DESCRIPTION Preemption is the interruption of normal operations to serve a preferred vehicle (e.g., train, emergency vehicle). This metric can be used to determine if preemption events are occurring as intended. • For rail preemption, the main priority is clearing track(s) of vehicles, with a secondary priority of minimizing delay for all transportation system users. • For emergency vehicle preemption, the main priority is reducing delay for the preferred vehicle. There are various metrics related to preemption that can be reported using high- resolution data, including the following: • Preempt request. The time when preempt requests were received for each preemption channel. • Preempt service. The time of service for each preemption channel. • Preemption details. The duration of preemption intervals for each preemption event. The type of interval information available depends on the type of preempt (i.e., rail or emergency vehicle) and the availability of certain inputs. Some potential intervals that can be tracked include entry delay, track clearance, gate down, dwell, time to service, max-out, and preempt input on/off. DETECTION NEEDS CALIBRATION REFERENCES Although high-resolution data from the controller can report this metric directly (without programming detection settings), specific detection equipment will be required at the intersection in order for vehicles to request preemption. In order to evaluate rail interactions accurately, an island circuit is needed so that actual train arrival times are recorded. N/A • Brennan et al. (2009) • Brennan et al. (2010) • UDOT PERFORMANCE MEASURE DETAILS 111

EXAMPLE USE Are vehicles being consistently cleared from the railroad tracks during the track clearance green interval? EXHIBIT 3-42 shows railroad preemption details overlaid with occupancy data for detection zones located between a signalized intersection and railroad tracks. Each column shows the following: • Preempt latency. The time between the advance warning preempt and beginning of the track clearance green interval. • Track clear green. When a green indication is given to the movement across the tracks, clearing vehicles from the tracks before a train arrives. • Preempt dwell. The time between when the track clearance green interval ends and the preempt ends (correlating to the train being through the crossing). During two cycles, at 16:52:50 and 16:53:44, there was a significant amount of occupancy throughout the preempt dwell interval, indicating that the track clearance green interval did not fully clear traffic between the stop bar and railroad tracks before the arrival of a train. This measure justified adding countermeasures at this particular site, including steerable signal heads and a gate-down circuit. EXHIBIT 3-42. PREEMPTION DETAILS EXAMPLE: DETAILS WITH DETECTOR OCCUPANCY (BRENNAN ET AL. 2009) Detector Occupancy Indicates Track Clearance Did Not Fully Clear Vehicles Between the Stop Bar and Railroad Tracks Before Arrival of a Train Detection Between Signalized Intersection Stop Bar and Railroad Tracks Preempt Latency Track Clear Green Preempt Dwell Detector Presence Forward of Tracks End of Active PreemptX T im e fr o m S ta rt o f A ct iv e P re em p t( s) 112 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

OBJECTIVES EQUIPMENT HEALTH VEHICLE DELAY VEHICLE PROGRESSION PEDESTRIANS BICYCLES RAIL EMERGENCY VEHICLES TRANSIT TRUCKS SAFETY 3.26 PRIORITY DETAILS DATA SOURCES VENDOR-SPECIFIC CENTRAL SYSTEM LOW- RESOLUTION AVI/AVL/SEGMENT SPEED CONTROLLER HIGH- RESOLUTION APPLICATIONS • Identify intersections with high numbers of priority events. • Identify intersections with priority events causing high delay for other transportation system users. 1 2 43 5 CATEGORY Co m m un ica tio n De te cti on In te rse cti on / Un co or di na te d T im ing Sy ste m/ Co or di na te d T im ing Ad va nc ed Sy ste m s an d A pp lic at ion s STAKEHOLDERS ORGANIZATIONAL PLANNING DESIGN AND CONSTRUCTION OPERATIONS MAINTENANCE PERFORMANCE MEASURE DETAILS 113

DESCRIPTION DETECTION NEEDS CALIBRATION REFERENCES Priority is the preferential treatment of one vehicle class (e.g., transit, trucks) over another (e.g., cars) at a signalized intersection, but unlike preemption, it will not disrupt coordination. The most common application is transit signal priority (TSP). High-resolution data events related to TSP include: • TSP check in • TSP adjustment to early green • TSP adjustment to extend green • TSP check out These data can facilitate the creation of a variety of metrics that can be used to examine the operation of priority control, including the frequency and duration of requests and the traffic signal response. Although high-resolution data from the controller can report this metric directly (without programming detection settings), specific detection equipment will be required at the intersection in order for vehicles to request priority. N/A • Feng, Figliozzi, and Bertini (2015) • Mackey (2016) • Sajjadi, Day, and Bright (2016) EXAMPLE USE 1 How many transit signal priority requests were made, and how were they served (i.e., early green or green extend)? EXHIBIT 3-43A is a plot of transit signal priority requests at a signalized intersection over a 24-hour period by priority number. Priority requests were evenly distributed between 5:00 AM and 12:00 AM at this location. EXHIBIT 3-43B shows how (i.e., early green or green extend) and when the priority requests were served. Priority service occurred between 6:00 AM and 9:00 PM, with most requests being accommodated using early green (versus green extend). EXHIBIT 3-43. PRIORITY DETAILS EXAMPLE: TRANSIT SIGNAL PRIORITY (MACKEY 2016) Even Distribution of Priority Requests Most Priority Requests Served with Early Green (A) Priority requests (B) Priority service Early Green Green Extended X X Early Green Green Extended X X 114 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

EXAMPLE USE 2 How often are priority requests being made on a bus rapid transit corridor? EXHIBIT 3-44 summarizes priority request details for a bus rapid transit (BRT) corridor. The number of priority requests by movement can be assessed using EXHIBIT 3-44A. Cycle-by- cycle details are provided for one direction in EXHIBIT 3-44B, including the time to transition to the priority phase and the amount of time dwelling in the priority phase. In this example, most priority requests are serviced within 10 seconds. EXHIBIT 3-44. PRIORITY DETAILS EXAMPLE: BUS RAPID TRANSIT CORRIDOR (COURTESY UTAH DEPARTMENT OF TRANSPORTATION) BRT Corridor Results in Many Priority Requests (A) Priority requests Most Priority Requests Serviced Within 10 Seconds (B) Details for Priority No. 6 Gate Down Input Off Input On Call Max-Out Dwell Time Track Clear Time to Service Delay X Preempt Request PERFORMANCE MEASURE DETAILS 115

3.27 REFERENCES 1. Brennan, T.M., C.M. Day, D.M. Bullock, and J.R. Sturdevant. 2009. “Performance measures for railroad preempted intersections.” Transportation Research Record, No. 2128, pp. 20-34. 2. Brennan, T.M., C.M. Day, J.R. Sturdevant, E. Raamot, and D.M. Bullock. 2010. “Track clearance performance measures for railroad preempted intersections.” Transportation Research Record, No. 2192, pp. 64-76. 3. Brennan, T.M., C.M. Day, J.R. Sturdevant, and D.M. Bullock. 2011. “Visual Education Tools to Illustrate Coordinated System Operation.” Transportation Research Record, No. 2259, pp. 59-72. 4. Day, C.M., E.J. Smaglik, D.M. Bullock, and J.R. Sturdevant. 2008. Real-Time Arterial Traffic Signal Performance Measures. Publication FHWA/IN/JTRP-2008/09. Joint Transportation Research Program, Indiana Department of Transportation, and Purdue University, West Lafayette, IN. 5. Day, C.M. and D.M. Bullock. 2009. “Application of High Resolution Traffic Signal Controller Data for Platoon Visualization and Optimization of Signal Offsets.” Presented at Mobil. TUM International Scientific Conference on Mobility and Transport, Munich, Germany. 6. Day, C.M. and D.M. Bullock. 2010. Arterial Performance Measures, Volume 1: Performance Based Management of Arterial Traffic Signal Systems. Final Report, NCHRP Project 03-79A, National Cooperative Highway Research Program, Transportation Research Board of the National Academies, Washington, DC. 7. Day, C.M., R. Haseman, H. Premachandra, T.M. Brennan, J.S. Wasson, J.R. Sturdevant, and D.M. Bullock. 2010. “Evaluation of arterial signal coordination: methodologies for visualizing high-resolution event data and measuring travel time.” Transportation Research Record, No. 2192, pp. 37-49. 8. Day, C.M. and D.M. Bullock. 2011. “Computational Efficiency of Alternative Algorithms for Arterial Offset Optimization.” Transportation Research Record, No. 2259, pp. 37-47. 9. Day, C.M., T.M. Brennan, A.M. Hainen, S.M. Remias, H. Premachandra, J.R. Sturdevant, G. Richards, J.S. Wasson, and D.M. Bullock. 2011. “Reliability, Flexibility, and Environmental Impact of Alternative Objective Functions for Arterial Offset Optimization.” Transportation Research Record, No. 2259, pp. 8-22. 10. Day, C.M., H. Premachandra, and D.M. Bullock. 2011. “Rate of Pedestrian Signal Phase Actuation as a Proxy Measurement of Pedestrian Demand.” Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., Paper No. 11-0220. 11. Day, C. and D.M. Bullock. 2014. Link Pivot Algorithm for Offset Optimization. Purdue University Research Repository. https://purr.purdue.edu/ publications/1745 12. Day, C.M., D.M. Bullock, H. Li, S.M. Remias, A.M. Hainen, R.S. Freije, A.L. Stevens, J.R. Sturdevant, and T.M. Brennan. 2014. Performance Measures for Traffic Signal Systems: An Outcome- Oriented Approach. Purdue University, West Lafayette, IN. http://dx.doi. org/10.5703/1288284315333 13. Day, C.M., D.M. Bullock, H. Li, S.M. Lavrenz, W.B. Smith, and J.R. Sturdevant. 2016. Integrating Traffic Signal Performance Measures into Agency Business Processes. Purdue University, West Lafayette, IN. http:// dx.doi.org/10.5703/1288284316063 14. Day, C.M., M. Taylor, J. Mackey, R. Clayton, S. Patel, G. Xie, H. Li, J.R. Sturdevant, and D.M. Bullock. 2016. “Implementation of Automated Traffic Signal Performance Measures.” ITE Journal, Vol. 86, Iss. 8, pp. 26-34. 116 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

15. Day, C.M. and D.M. Bullock. 2017. “Visualization of the potential performance of coordinated systems to support management of signal timing.” Presented at 96th Annual Meeting of the Transportation Research Board, Washington, D.C., Paper No. 17-00090. 16. Day, C.M., S.E. Young, D.M. Bullock, and D.S.T. Fong. 2017. Sensor Fusion and MOE Development for Off-Line Traffic Analysis of Real Time Data. Final Report, FHWA SBIR DTFH61-14-C-00035, Purdue University, West Lafayette, IN. https://doi.org/10.5703/1288284316556 17. Feng, W., M. Figliozzi, and R. Bertini. 2015. “Empirical evaluation of transit signal priority: Fusion of heterogeneous transit and traffic signal data and novel performance measures.” Transportation Research Record, No. 2388, pp. 20-31. 18. Freije, R., A.M. Hainen, A. Stevens, H. Li, W.B. Smith, C.M. Day, J.R. Sturdevant, and D.M. Bullock. 2014. “Graphical performance measures for practitioners to triage split failure trouble calls.” Transportation Research Record, No. 2439, pp. 27-40. 19. Gettman, D., M. Abbas, H. Liu, and A. Skabardonis. 2012. NCHRP Web- Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 1: Practitioner Guidance. Transportation Research Board, Washington, DC. http://dx.doi. org/10.17226/22290 20. Gettman, D., G. Madrigal, S. Allen, T. Boyer, S. Walker, J. Tong, S. Phillips, H. Liu, X. Wu, H. Hu, M. Abbas, Z. Adam, and A. Skabardonis. 2012. NCHRP Web-Only Document 202: Operation of Traffic Signal Systems in Oversaturated Conditions, Volume 2: Final Report. Transportation Research Board, Washington, DC. http://dx.doi. org/10.17226/22289 21. Highway Capacity Manual: A Guide for Multimodal Mobility Analysis, 6th ed. 2016. Transportation Research Board, Washington, DC. 22. Hu, J., M.D. Fontaine, and J. Ma. 2016. “Quality of Private Sector Travel- Time Data on Arterials.” Journal of Transportation Engineering, Vol. 142, No. 4, Article ID 04016010. 23. Hubbard, S.M.L., D.M. Bullock, and C.M. Day. 2008. “Integration of real- time pedestrian performance measures into existing traffic signal system infrastructure.” Transportation Research Record, No. 2080, pp. 37-47. 24. Institute of Transportation Engineers (ITE). 2008. Using Existing Loops at Signalized Intersections for Traffic Counts. 25. Krohn, D., L. Rymarcsuk, J.K. Mathew, C.M. Day, H. Li, and D.M. Bullock. 2017. “Outcome assessment using connected vehicle data to justify signal investments to decision makers.” Submitted to 96th Annual Meeting of the Transportation Research Board. Paper No. 17-00314. 26. Lavrenz, S. 2015. High-Resolution, Data- Based Methods for Enhanced Asset Preservation, Mobility, and Safety at Signalized Intersections. PhD Thesis, Purdue University, West Lafayette, IN. 27. Lavrenz, S.M., C.M. Day, A.M. Hainen, W.B. Smith, A.L. Stevens, H. Li, and D.M. Bullock. 2015. “Characterizing signalized intersection performance using maximum vehicle delay.” Transportation Research Record, No. 2488, pp. 41-52. 28. Lavrenz, S.M., C. Day, J. Grossman, R. Freije, and D.M. Bullock. 2016. “Use of high resolution signal controller data to identify red light running.” Transportation Research Record, No. 2558, pp. 41-53. 29. Li, H., A.M. Hainen, C.M. Day, G. Grimmer, J.R. Sturdevant, and D.M. Bullock. 2013. “Longitudinal performance measures for assessing agency-wide signal management objectives.” Transportation Research Record, No. 2355, pp. 20-30. PERFORMANCE MEASURE DETAILS 117

30. Li, H., L.M. Richardson, C.M. Day, J. Howard, and D.M. Bullock. 2017. “Scalable split failure identification dashboard and split time improvement heuristic.” Transportation Research Record, No. 2620, pp. 83-95. 31. Liu, H.X., W. Ma, X. Wu, and H. Hu. 2008. Development of a Real-Time Arterial Performance Monitoring System Using Traffic Data Available from Existing Signal Systems. Report MN/RC 2009-01, Minnesota Department of Transportation. 32. Liu, H.X. and W. Ma. 2009. “A virtual probe vehicle model for time-dependent travel time estimation on signalized arterials.” Transportation Research Part C: Emerging Technologies, Vol. 17, pp. 11-26. 33. Mackey, J. 2016. “UDOT Signal Performance Metrics: New and Upcoming Metrics.” Presented at Automated Traffic Signal Performance Measures Workshop, Salt Lake City, UT. http://docs.lib.purdue.edu/ atspmw/2016/Presentations/8/ 34. Mackey, J. 2017. “UDOT Automated Traffic Signal Performance Measures Configuration Utility.” Presented at UDOT ATSPM Train-the-Trainer Workshop, Salt Lake City, UT. http://udottraffic.utah.gov/ ATSPM/Images/TTTJamieMackey.pdf 35. Mathew, J., D. Krohn, H. Li, C. Day, and D. Bullock. 2017. Implementation of Probe Data Performance Measures. Report PA-2017-001-PU-WO 001, Pennsylvania Department of Transportation. 36. Quayle, S.M., P. Koonce, D. DePencier, and D.M. Bullock. 2010. “Arterial performance measures with media access control readers: Portland, Oregon, pilot study.” Transportation Research Record, No. 2192, pp. 185-193. 37. Richardson, L.M., M.D. Luker, C.M. Day, M. Taylor, and D.M. Bullock. 2017. “Outcome assessment of peer-to-peer adaptive control adjacent to a national park.” Transportation Research Record, No. 2620, pp. 43-53. 38. Robertson, D.I. 1969. Transyt: a Traffic Network Study Tool. Report No. LR 253, Road Research Laboratory, Crowthorne, Berkshire, England. 39. Sajjadi, S., C.M. Day, and K. Bright. 2016. “Evaluating transit signal priority and offset optimization strategies in microsimulation using the Purdue Coordination Diagram.” Presented at 95th Annual Meeting of the Transportation Research Board, Washington, D.C., Paper No. 16-6760. 40. Sharifi, E., S.E. Young, S. Eshragh, M. Hamedi, R.M. Juster, and K. Kaushik. 2016. “Quality Assessment of Outsourced Probe Data on Signalized Arterials: Nine Case Studies in Mid- Atlantic Region.” Presented at 95th Annual Meeting of the Transportation Research Board, Washington, DC. 41. Sharma, A., D.M. Bullock, and J.A. Bonneson. 2007. “Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections.” Transportation Research Record, No. 2035, pp. 69-80. 42. Shelby, S.G., D. Gettman, L. Head, D.M. Bullock, and N. Soyke. 2007. “Data- driven algorithms for real-time adaptive tuning of offsets in coordinated traffic signal systems.” Transportation Research Record, No. 2035, pp. 1-9. 43. Smaglik, E.J., D.M. Bullock, and A. Sharma. 2007. “A Pilot Study on Real- Time Calculation of Arrival Type for Assessment of Arterial Performance,” Journal of Transportation Engineering, Vol. 133, pp. 415-422. 44. Smaglik, E.J., A. Sharma, D.M. Bullock, J.R. Sturdevant, and G. Duncan. 2007. “Event-based data collection for generating actuated controller performance measures.” Transportation Research Record, No. 2035, pp. 97–106. 118 PERFORMANCE-BASED MANAGEMENT OF TRAFFIC SIGNALS

45. Smaglik, E.J., D. Gettman, D.M. Bullock, C.M. Day, and H. Premachandra. 2011. “Comparison of alternative real-time performance measures for measuring signal phase utilization and identifying oversaturation.” Transportation Research Record, No. 2259, pp. 123-131. 46. Smith, W.B. 2014. Signalized Corridor Assessment. MSCE thesis, Purdue University, West Lafayette, IN. 47. Sunkari, S.R., H.A. Charara, and P. Songchitruska. 2012. “Portable toolbox for monitoring and evaluating signal operations.” Transportation Research Record, No. 2311, pp. 142-151. 48. Taylor, M. 2016. “SPM Basics and Applications Overview.” Presented at Automated Traffic Signal Performance Measures Workshop, Salt Lake City, UT. http://dx.doi. org/10.5703/1288284316030 49. Talukder, M.A.S., A.M. Hainen, S.M. Remias, and D.M. Bullock. 2017. “Route- Based Mobility Performance Metrics Using Probe Vehicle Travel Times.” Presented at 96th Annual Meeting of the Transportation Research Board, Washington, DC, Paper No. 17-05076. 50. Urbanik, T. et al. 2015. NCHRP Report 812: Signal Timing Manual, 2nd ed. Transportation Research Board of the National Academies, Washington, DC. 51. Utah Department of Transportation (UDOT). (n.d.-a). Automated Traffic Signal Performance Measures website. http://udottraffic.utah.gov/atspm/ 52. Wasson, J.S., J.R. Sturdevant, and D.M. Bullock. 2008. “Real-Time Travel Time Estimates Using Media Access Control Address Matching.” ITE Journal, Vol. 78, Iss. 6, pp. 20-23. 53. Wu, X., H. Liu, and D. Gettman. 2010. “Identification of Oversaturated Intersections Using High-Resolution Traffic Signal Data.” Transportation Research Part C, Vol. 18, pp. 626-638. 54. Young, S.E., E. Sharifi, C.M. Day, and D.M. Bullock. 2017. “Visualizations of travel time performance based on vehicle reidentification data.” Transportation Research Record, No. 2646, pp. 84-92. 55. Zhao, M., A. Sharma, E. Smaglik, and T. Overman. 2015. “Traffic signal battery backup systems: use of event-based traffic controller logs in performance- based investment programming.” Transportation Research Record, No. 2488, pp. 53-61. PERFORMANCE MEASURE DETAILS 119

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Management of traffic signal systems is a critical function for many transportation agencies. Thanks to advancements in technology, it is now possible to collect large amounts of data at signalized intersections, leading to the development of dozens of performance measures.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 954: Performance-Based Management of Traffic Signals provides information to help agencies invest in signal performance measures as part of a comprehensive approach to performance-based management.

Supplementary materials to the report include a data dictionary and a PowerPoint presentation.

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