Developing a Guide to Effective Methods for Setting Transportation Performance Targets (2023) / Chapter Skim
Currently Skimming:
Pages 4-34
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 4... ...
4 C H A P T E R 2 Phase I: Foundational Research Research Approach Phase I of this research involved two components: Task 1: Review Available Literature. Task 1 involved a review of target setting methods as documented in various sources.
|
From page 5... ...
5 2. Level of sophistication – Our selection skewed toward more sophisticated approaches, while still seeking to understand the circumstances and rationales used when less sophisticated approaches were employed.
|
From page 6... ...
6 versions 1 and 2 of the National Performance Management Research Data Set (NPMRDS) , setting achievable targets based on baseline (2017)
|
From page 7... ...
7 forecast anticipated performance or combined multiple approaches (sometimes by averaging results) in order to select a target.
|
From page 8... ...
8 State Targeted Reduction Trend Analysis Trend with Adjustment Model Model with Adjustment Group Discussion/ Unknown Kentucky X Louisiana X Maine X Maryland X Massachusetts X Michigan X Minnesota X Mississippi X Missouri X Montana X Nebraska X Nevada X New Hampshire X New Jersey X New Mexico X New York X North Carolina X North Dakota X Ohio X Oklahoma X Oregon X Pennsylvania X Puerto Rico X Rhode Island X South Carolina X South Dakota X Tennessee X Texas X Utah X Vermont X Virginia X Washington X West Virginia X Wisconsin X Wyoming X Total 22 11 11 4 1 3 For the safety targets, methods were defined as: • Targeted reduction - A defined decrease from the baseline, often based on policy; • Trend analysis – A forecast based on historical performance trend;
|
From page 9... ...
9 • Trend with adjustment – A trend analysis with an adjustment to account for other factors; • Model – A regression analysis or tool developed to account for various factors to predict performance; • Model with adjustment – A model approach with an external adjustment to account for other factors; and • Group discussion /unknown – Group discussion with a multidisciplinary working group and/or other stakeholders or other undefined method. Table 2.
|
From page 10... ...
10 State Trend Analysis Trend Plus Future Funding Model Scenario Analysis Other/ Unknown New York State X North Carolina X North Dakota X Ohio X Oklahoma X Oregon X Pennsylvania X Puerto Rico X Rhode Island X South Carolina X South Dakota X Tennessee X Texas X Utah X Vermont X Virginia X Washington State X West Virginia X Wisconsin X Wyoming Total 3 9 31 2 6 For the pavement condition targets, methods were defined as: • Trend analysis – A forecast based on historical performance trend; • Trend plus future funding – A time-series trend analysis that also accounts for anticipated funding levels; • Model or system-based – Using an asset management-based system (e.g., pavement management system) ; • Scenario analysis – Using an asset management system to predict conditions, but analyzing multiple funding levels or strategies for prioritizing funding; and • Other/unknown – Other included using the baseline, making an adjustment off the baseline, adopting minimum standards, or other undefined method.
|
From page 11... ...
11 Table 3. Overview of Analysis Approaches for Establishing Bridge Condition Targets, 2018 Submittals State Trend Analysis Trend Plus Future Funding Model Scenario Analysis Other/ Unknown Alabama X Alaska X Arizona X Arkansas X California X Colorado X Connecticut X Delaware X District of Columbia X Florida X Georgia X Hawaii X Idaho X Illinois X Indiana X Iowa X Kansas X Kentucky X Louisiana X Maine X Maryland X Massachusetts X Michigan X Minnesota X Mississippi X Missouri X Montana X Nebraska X Nevada X New Hampshire X New Jersey X New Mexico X New York State X North Carolina X North Dakota X Ohio X Oklahoma X Oregon X Pennsylvania X Puerto Rico X Rhode Island X
|
From page 12... ...
12 State Trend Analysis Trend Plus Future Funding Model Scenario Analysis Other/ Unknown South Carolina X South Dakota X Tennessee X Texas X Utah X Vermont X Virginia X Washington State X West Virginia X Wisconsin X Wyoming X Total 6 6 32 3 5 For bridge condition measures, methods were defined the same way as for pavement condition measures.
|
From page 13... ...
13 Table 4. Overview of Analysis Approaches for Establishing Travel Time Reliability and Freight Reliability Targets, 2018 Submittals State Baseline with Assumptions Trend Analysis Trend Plus Other Factors Performance Risk Analysis Segment Risk Analysis Model Group Discussion + Analysis / Unknown Alabama X Alaska X Arizona X Arkansas X California X Colorado X Connecticut X Delaware X District of Columbia X Florida X Georgia X Hawaii X Idaho X Illinois X Indiana X Iowa X Kansas X Kentucky X Louisiana X Maine X Maryland X Massachusetts X Michigan X Minnesota X Mississippi X Missouri X Montana X Nebraska X Nevada X New Hampshire X New Jersey X New Mexico X New York X North Carolina X North Dakota X Ohio X
|
From page 14... ...
14 State Baseline with Assumptions Trend Analysis Trend Plus Other Factors Performance Risk Analysis Segment Risk Analysis Model Group Discussion + Analysis / Unknown Oklahoma X Oregon X Pennsylvania X Puerto Rico X Rhode Island X South Carolina X South Dakota X Tennessee X Texas X Utah X Vermont X Virginia X Washington X West Virginia X Wisconsin X Wyoming X Total 13 7 19 2 3 5 2 For travel time reliability and freight reliability measures, methods were defined as follows: • Building off baseline, with assumptions – Maintaining the baseline level as the target or making an adjustment based on judgement; • Trend analysis – A forecast based on historical performance trend; • Trend plus other factors – A trend analysis but with adjustments to account for other factors that may affect future performance; • Performance risk analysis – Using monthly performance data to calculate a standard deviation and then use the deviation to assess confidence level in order to set a target; • Segment risk analysis – Using segment-level data to assess segment that are at risk of shifting across the threshold of a "reliable" segment; • Model – A regression analysis of tool developed to account for various factors to predict performance, typically applied at the segment level; and • Group discussion and analysis or unknown – Engagement with stakeholders, which can rely upon data and analysis using any of the other approaches but was not clearly defined.
|
From page 15... ...
15 Table 5. Overview of Analysis Approaches for Establishing Non-SOV Targets, 2018 Submittals State UZA Trend Analysis Trend Plus Other Factors Policybased Model Other/Unknown Arizona Phoenix-Mesa, AZ X Arkansas Memphis, TNMS-AR X California Los AngelesLong BeachAnaheim, CA X Riverside-San Bernardino, CA X Sacramento, CA X San Diego, CA X San FranciscoOakland, CA X San Jose, CA X Colorado Denver-Aurora, CO X Delaware Philadelphia, PANJ-DE-MD X District of Columbia Washington, DCVA-MD X Georgia Atlanta, GA X Illinois Chicago, IL-IN X Indiana Indianapolis, IN X Maryland Baltimore, MD X Massachusetts Boston, MA-NHRI X Michigan Detroit, MI X Minnesota Minneapolis-St.
|
From page 16... ...
16 State UZA Trend Analysis Trend Plus Other Factors Policybased Model Other/Unknown Oregon Portland, OR-WA X Pennsylvania Pittsburgh, PA X Texas Dallas-Fort Worth-Arlington, TX X Houston, TX X Utah Salt Lake CityWest Valley City, UT X Washington Seattle, WA X Wisconsin Milwaukee, WI X Total 12 11 2 2 6 For the non-SOV mode share measure, methods were defined as follows: • Trend analysis – A forecast based on historical performance trend; • Trend plus other factors – A trend analysis but with adjustments to account for other factors that may affect future performance; • Policy-based – While the analysis of trends may have been conducted, the target itself was set based on a policy direction to increase non-SOV mode share; • Model – Using a regional travel model to forecast future mode share, often with the anticipated change applied to the baseline mode share; and • Other/Unknown – Other included undefined methods.
|
From page 17... ...
17 Table 6. Overview of Analysis Approaches for Establishing PHED per Capita Targets, 2018 Submittals State UZA Baseline with Assumptions Trend Analysis Trend Plus Other Factors Model Other/Unknown Arizona PhoenixMesa, AZ X Arkansas Memphis, TNMS-AR X California Los AngelesLong BeachAnaheim, CA X Riverside-San Bernardino, CA X Sacramento, CA X San Diego, CA X San FranciscoOakland, CA X San Jose, CA X Colorado DenverAurora, CO X Delaware Philadelphia, PA-NJ-DE-MD X District of Columbia Washington, DC-VA-MD X Georgia Atlanta, GA X Illinois Chicago, IL-IN X Indiana Indianapolis, IN X Maryland Baltimore, MD X Massachusetts Boston, MANH-RI X Michigan Detroit, MI X Minnesota MinneapolisSt.
|
From page 18... ...
18 State UZA Baseline with Assumptions Trend Analysis Trend Plus Other Factors Model Other/Unknown North Carolina Charlotte, NCSC X Ohio Cincinnati, OH-KY-IN X Cleveland, OH X Columbus, OH X Oregon Portland, ORWA X Pennsylvania Pittsburgh, PA X Texas Dallas-Fort WorthArlington, TX X Houston, TX X Utah Salt Lake CityWest Valley City, UT X Washington Seattle, WA X Wisconsin Milwaukee, WI X Total 2 8 13 2 7 For the PHED per capita measure, methods were defined as follows: • Building off baseline with assumptions – Maintaining the baseline level as the target or making an adjustment based on judgement; • Trend analysis – A forecast based on historical performance trend; • Trend plus other factors – A trend analysis but with adjustments to account for other factors that may affect future performance; • Policy-based – While the analysis of trends may have been conducted, the target itself was set based on a policy direction to increase non-SOV mode share; • Model – Using a regional travel model to forecast future congestion, often with the anticipated change applied to the baseline PHED; and • Other/Unknown – Other included undefined methods. Summary of Phase I Focus Groups and Interviews Overview In order to supplement the literature review, the research team conducted focus groups and one-on-one interviews with state DOTs and MPOs to learn more about the data analysis and methods used to set targets.
|
From page 19... ...
19 The research team conducted the following focus groups and interviews: • PM1: Safety Measures (state DOTs) o Group 1: Louisiana, Delaware, Montana, Florida o Group 2: Texas, New Mexico, Virginia, Iowa o Group 3: Michigan, South Carolina, Colorado • PM2: Infrastructure Conditions Measures (state DOTs)
|
From page 20... ...
20 • "Philosophy" of whether targets should be ambitious or realistic and alignment with other targets • Communication challenges around setting increasing targets and not meeting ambitious targets • Coordination on target setting • Specific internal or external influences the targets consider • Use of data for analysis of crash causes and actions the agency will take to address them. Summary of Focus Group 1: Non-Technical Philosophy.
|
From page 21... ...
21 Florida. Florida DOT (FDOT)
|
From page 22... ...
22 itself. Virginia was the clear outlier on considering internal and external influences, with a model that incorporates 14 different factors.
|
From page 23... ...
23 Summary of Focus Group 3: Highly Technical Approaches Philosophy. All the states in this group mentioned the use of models in setting safety targets, and in line with this the participants all expressed the importance of a "data-informed" approach to setting their targets, even if their states have a vision zero target more broadly.
|
From page 24... ...
24 Description of Safety Target Setting Methods Colorado. Colorado has had notable increases in fatalities and serious injuries between 2013 and 2018, when the trend reversed slightly, driven by increases in factors such as population and VMT growth, legalization of marijuana, and a thriving economy.
|
From page 25... ...
25 • All participating agencies reported that technical challenges related to data quality, and discontinuities between historical data formats and the national highway performance (NHP) measures for pavement and bridge conditions, contributed to them to establishing relatively conservative targets.
|
From page 26... ...
26 To establish bridge management strategies, Arizona DOT looks at impacts 10 or more years out. The slow deterioration rates of bridges make changes in the near term very small.
|
From page 27... ...
27 South Dakota. South Dakota DOT (SDDOT)
|
From page 28... ...
28 PM3: Reliability, Freight, Congestion, and CMAQ Emissions Reductions Measures Separate focus groups/interviews were held for the reliability and freight measures and for the NonSOV and PHED measures. To accommodate schedules of participants, the PM3 focus groups were broken down as follows, including some individual interviews and e-mail exchanges: • Reliability and Freight Measures Focus Group 1: Iowa DOT, NMDOT, SCDOT, TxDOT (10/26/2020)
|
From page 29... ...
29 Description of Travel Time Reliability and Freight Reliability Target Setting Methods Alabama. Alabama DOT (ALDOT)
|
From page 30... ...
30 Iowa also indicated its system is nearly 100% reliable, which makes a statewide target for reliability of limited value and meaning. The state is therefore focusing attention on its few unreliable segments.
|
From page 31... ...
31 differing statistical models did not make a significant difference. One variable it considered was the impact of paving projects, but its analysis did not yield a conclusive result.
|
From page 32... ...
32 Description of Urbanized Area (Non-SOV and PHED per Capita) Target Setting Methods Memphis MPO (Memphis, TN-MS-AR UZA)
|
From page 33... ...
33 For the mid-performance period, the Philadelphia regional partners opted not to adjust targets, mainly due to the pandemic. DVRPC suggested the forecasts from its travel demand model is no longer valid because travel patterns have significantly changed.
|
From page 34... ...
34 capital improvements and their impact on delay, but ultimately decided none would have a significant impact on PHED. The agencies decided to be conservative for the first round of target setting, with the knowledge they could revisit the measures in 2020.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.