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SPF Development in Illinois

SPF Development in Illinois. Yanfeng Ouyang Department of Civil & Environmental Engineering University of Illinois at Urbana-Champaign. Outline. Background Methodology review SPF Development In Illinois Data preparation SPFs development SPF Applications (Kim Kolody, Session 4)

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SPF Development in Illinois

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  1. SPF Development in Illinois Yanfeng Ouyang Department of Civil & Environmental Engineering University of Illinois at Urbana-Champaign

  2. Outline • Background • Methodology review • SPF Development In Illinois • Data preparation • SPFs development • SPF Applications • (Kim Kolody, Session 4) • Uses of SPF and PSI Values • (David Piper, Session 9)

  3. Crash # SPF Other contributing factors expected crash # AADT Background • Safety performance functions (SPFs) • Descriptive statistical relationships between crash counts and contributing factors (e.g., traffic volume) • Developing SPFs helps • identify high-potential candidate locations for safety improvement • prepare for implementation of various safety tools

  4. Model Specifications • Lognormal Regression Models • log(crash count) follows normal distribution • Ordinary least-squares estimation • Loglinear Regression Models • Poisson models • Describes discrete, rare events • Poisson distribution (variance = mean) • Negative binomial models • Negative binomial distribution • Overdispersion parameter, k • Maximum likelihood estimation

  5. Explanatory Variables • Quantitative • Values that represent a condition, characteristic, or quantity • Can be directly entered into SPF • E.g., AADT, lane width, # lanes, etc. • Categorical • Non-numerical variables to describe a situation • Use binary ‘dummies’, or define “peer groups” • E.g., median type, shoulder type, terrain type, etc.

  6. SPF Types • Level-I SPF • Determine crash count based only on traffic volumes (AADT) • From past studies, AADT has the largest impacts on crashes • Level-II SPF • Multivariate analysis that explicitly includes other variables • Can be used for education and enforcement purposes

  7. Development • Segment Length Selection • Entire homogeneous segment with variable lengths • Break segments into small sections • Sliding window approach • Intersection Crashes • Crashes that are • “at an intersection” • “intersection related, but not at an intersection” • “not intersection related” • Crashes within 250 feet of an intersection (SafetyAnalyst)

  8. The Illinois Experience • Illinois is committed to reducing fatalities andsevere injuries on roadways • Focus on crasheswith fatality and severe injuries (Ks, As, Bs) • Roadway site types • Roadway segments (homogeneous segments) • Intersections • Model specification • Type-I SPFs • Negative binomial model

  9. Overview of Datasets • Five years of crash data (2001-2005) on U.S. and state marked and unmarked routes • Roadway data from Illinois Roadway Inventory System (IRIS) • 60,240 segments (16421 miles) • 54,880 intersections (state-state, state-local) • Crash data • 2,826 records of K (fatal) • 26,768 records of A (disabling injury) • 65,654 records of B (evident injury) • Intersection treatment • Consider crashes within 250 feet of an intersection (SafetyAnalyst) • Cross roads often lack roadway data • Local cross roads, use Average AADT in each county for various area types (provided by IDOT) • State-maintained minor cross roads, use the average AADT of the minor route for the County and Township near the intersection

  10. Peer Groups (Adapted from Illinois Five Percent Report, 2008)

  11. Data Preparation • RoadwaySite Definition • - GIS Roadway Data: Inventory Number • - Crash Data: TS Route Number • Positioning System • - GIS Roadway Data: Station • - Crash Data: Milepost • IDOTprovides a translation table to convert the TS Route and Milepost into Key Route Number and Station, for each year

  12. Data Preparation Categorize segments/intersection by peer group

  13. Data Preparation • Required Fields in the Input Data • Inventory Number, Beginning Station, Ending Station, AADT, AADT Year, Road Name, Segment Length, Roadway Functional Class, County Name, Township/Municipality Name, Peer Group, Matched Crashes (K, A, and B) Type-A Crashes, Rural Two-Lane Highway (Segment Peer group 1)

  14. SPF Development Example Segment Functional form: Maximum Likelihood Estimation (MLE) in SAS Estimation for 12 peer groups and four severity types (K, A, B and K+A+B)

  15. PSI Calculation Weighted PSI (Potential for Safety Improvements) PSI – how much a site’s safety performance exceeds the expectation Empirical Bayesian (EB) Method: Find a weighted average of the predicted and observed numbers of crashes Default values of weights: Fatal-K (25), Injury-A (5), and Injury-B (1)

  16. PSI Calculation Example Network Screening with Weighted PSI Each road segment has a weighted PSI value per segment length List road segments in descending order of weighted PSI values 16

  17. Process Automation 17

  18. Other Related Work • Multivariate SPF development • Implementation the SPFs in local safety tools • Utilization and applications of SPFs • (Kim Kolody, Session 4) • (David Piper, Session 9)

  19. Thank you! yfouyang@illinois.edu 217-333-9858

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