1. Use and Abuse of Crash Data in Roadway Access Management A Workshop at the National Access Management Conference Baltimore, Maryland July 13, 2008

2. Instructor Team Instructor team introductions – David Plazak – Zach Hans – James Sun – Eric Fitzsimmons 2

3. Workshop Overview The topic of this workshop was suggested by attendees at the Park City, Utah National Access Management Conference in 2006 Persons who suggested the topic indicated that they would like to be able to use available crash data to evaluate access management plans and to market access management as a way to improve safety This workshop has been designed to be very “hands- on”—participants should be able to immediately apply many of the concepts they learn 3

4. Participant Introductions Brief participant introductions – Your name – Your involvement with access management We’d like to try to get a good mixture of level of experience at the tables for the interactive exercises 4

5. Parts of Today’s Workshop Workshop schedule: – Part one: Time: 1:00 PM to 2:20 PM Workshop introduction and objectives Key access management safety concepts, including crash reduction factors A hands-on problem: fix an “access management mess” where all tables have complete data to work with – Part two: Time: 2:35 PM to 4:30 PM Data quality considerations Some data problems that might be encountered Hands-on problem #2: Fix an “access management mess” where tables have different levels of data completeness and quality to work with Workshop wrap-up 5

6. Workshop Objectives Provide the participants with a good working knowledge of: – Crash types associated with lack of sound access management – Typical crash reduction factors associated with access management treatments – Crash data and potential weaknesses of crash data 6

7. Your Interest in Attending this Workshop? Let’s go around the room quickly and compile a list of things you’d most like to learn during this workshop Each table will provide one new idea, then we’ll move on to the next table until we run out of ideas 7

8. Data-Driven Access Management Access management treatments and plans should be directly tied to measurable objectives such as crash rate or crash cost reduction Access management treatments proposed should be appropriate given the types of crashes and pattern of crashes being experienced in a corridor Access management treatment costs need to be justifiable based upon the expected benefits of crash reductions and other objectives Stakeholders and decision-makers must be convinced that the “gain” of access management is worth the “pain” Confidence in both past (“before treatment”) and expected future crash rates (“after treatment”) should be high You want to be very sure that any treatments will produce a noticeable and positive result 8

9. Access Management and Safety Most access-management related crashes occur on urban and suburban arterial roadways at speeds of 35 to 55 miles per hour Up to half of all crashes in urban areas are related to issues of access (minor public road intersections, traffic signal spacing, driveways) Although most access-related crashes occur in urban or suburban areas, access-related crashes in rural areas tend to be severe crashes due to higher travel speeds Access-related crashes occur at conflict points The diagram represents one crash data point 9

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11. Conflict Points Diverging Merging Crossing 11

12. Examples of Conflict Point Reduction Treatments 12

13. Driveway Crash Pattern Left turn movements generate ¾ of all crashes at driveways Left turn entering movements generate almost ½ of all crashes at driveways Source: Federal Highway Administration 13

14. Common Types of Access Management Traffic signal spacing Marginal access management – Management of access features at (and beyond) the roadway right of way line Examples: controlling driveway location and minimum spacing between driveways Medial access management – Management of access features in the center of the roadway Examples: median types and median openings Separation of turning traffic from through traffic streams Example: dedicated left-turn lanes or bays 14

15. Effectiveness of Traffic Signal Spacing Reducing signals from 4 per mile (1/4 mile spacing) to 2 per mile (1/2 mile spacing) will reduce the total crash rate by up to 50%* – (This will also have a significant impact on quality of traffic flow during peak hours) Uniform traffic signal spacing is safer and more efficient than non-uniform spacing *The source for all crash reduction factors is the National Highway Institute course “Access Management: Location And Design” 15

16. Effectiveness of Marginal Access Management Treatments Applying total access control (e.g. allowing no direct driveway accesses) – Reduces crash rates by 40-45% on median divided roadways Managing access density (driveways per mile) – Reducing access density by 20-25% can reduce total crash rates by 25-40% 16

17. Effectiveness of Medial Access Control Treatments Adding a two-way left turn lane to an undivided 4 lane roadway will reduce crash rates by 20-25% – Pedestrian crash rates will not change Adding a non-traversable (raised) median to an undivided 4 lane roadway will reduce crash rates by 40-45% – Pedestrian crash rates will decrease by 50% 17

18. Effectiveness of Left Turn Lanes or Bays Adding a left-turn bay at a busy un-signalized urban or suburban intersection may reduce crash rates by up to 70% Adding a left turn bay at a busy signalized urban or suburban intersection or at a busy rural un-signalized intersection may reduce crash rates by 40-50% 18

19. Problem 1: Fix This Mess South Ankeny Blvd., Ankeny, Iowa 19

20. What Do Crash Data Really Look Like? 20

21. Crash Rate Calculation C = Number of crashes over Y years Y = Number of years being evaluated AADT = Average annual daily traffic Links Nodes (Intersections) M = Hundred million vehicle miles/year (MHVM) for linksM = Million entering vehicles/year (MEV) 21

22. What’s On Your Table … 22 Land Use Crash data tables and charts Crash data stack mapLaminated base map Traffic over time Corridor photos 22

23. An Example Plan … 23

24. Brief Table Reports … What Treatments Do You Recommend? 24

25. Break 25

26. Part Two Data quality considerations Some data problems that might be encountered Hands-on problem #2: Fix an “access management mess” where tables have different levels of data completeness and quality to work with Workshop wrap-up 26

27. Think About It … (to discuss later, if we have time) 1. How do you use crash data? 2. What is important to you about crash data? 3. What are some of the concerns/problems you experience in using crash data? 27

28. Crash Data Allow Better … Problem Identification Understanding of the problem before jumping into exploring and designing solutions Focus on severe crashes rather than all (minor) crashes However … 28

29. You Need Good Quality Data The Ingredients Matter: Quality Control 29

30. The Characteristics of Data Quality (The “Six-Pack”) 30 Data AccessibilityIntegration Consistency Uniformity CompletenessAccuracy Timeliness

31. Crash Data Quality: Timeliness Sometimes crash data are not available for months or even years Varying timeliness of different jurisdictions can cause issues for comparative analysis Time itself is important – did something change during the analysis period? Also – the time period is important … one year of data are probably not enough! 31

32. Crash Data Quality: Accuracy Spatial Location Attributes, e.g., severity, crash type, roadway info 32 SOUTH ANKENY BOULEVARD 1 ST Road v Original Considering functional area

33. Crash Data Quality: Completeness Missing data can lead to a misleading picture and erroneous conclusions Some crash records have “unknown” or “other” fields Some crash records are missing altogether Variations between jurisdictions (county level, state level) can lead to inaccuracies in comparative analysis 33 Collision Type Num of CrashesPercentage Non-collision1785432.6% Head-on10061.8% Rear-end1214322.2% Angle, oncoming left turn35286.4% Broadside1019218.6% Sideswipe, same direction50359.2% Sideswipe, opposite direction11452.1% Unknown35386.5% Not Reported3740.7% Total54815100.0%

34. Crash Data Quality: Consistency/Uniformity Across jurisdictions Across time Consistent severities 34

35. Crash Data Quality: Integration Integration provides a ‘richer’, more complete source of information (e.g., integration with roadway features) Double check on accuracy (including severity) 35

36. Crash Data Quality: Accessibility How can you get crash data? How easy is it to get? What form do you want it in? Continuum: not available … special request w/delay … regular updates … service … instant web access 36

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38. Typical Crash Data Issues These may not be apparent to the data user

39. Changes in Crash Forms Content – Addition/elimination of attributes collected – Change in definitions (values) Non-collision Head-on Rear-end Angle, oncoming left turn Broadside Sideswipe, same direction Sideswipe, opposite direction Head-on Broadside/Left Turn Rear End Rear End/Right Turn Rear End/Left Turn Sideswipe/Opposite Direction Sideswipe/Same Direction Sideswipe/Right Turn Sideswipe/Left Turn Sideswipe/Dual Left Turn Sideswipe/Dual Right Turn Broadside/Right Angle Broadside/Right Entering Broadside/Left Entering Head-on/Left Entering Sideswipe/Both Left Turning Single Pedestrian Bicycle Parked Vehicle BeforeAfter Collision Type 39

40. Changes in Crash Forms, cont. Impacts: Difficult to perform direct comparisons over analysis period. May result in systematic change in apparent crash performance, e.g. crash reduction. Year Crash Rate Statewide Year Crash Rate Site #1 Change in crash form 40

41. Cartographic (Base Map) Changes Shift, update to reference road network Impact: Challenging to systematically assign crash location. 41

42. Location Accuracy How are the crashes located? – GPS (where?) – Manually derived, based on literal description – LRS, Link-node, other? What reference networks are used? – GIS – LRS – Link-node 42

43. Location Accuracy, cont. How do accuracies vary among location methods and reference networks? – Ex. GPS ±5m v. GIS-based road network ±10m Impact: type I or type II errors – you’d not know X Actual crash location Crash may be located anywhere within this area. Roadway may be presented anywhere within this area. X Geocoded crash location GIS road network 43

44. Changes in Statute Reportable crash definition – Property damage threshold, e.g. $500 v. $1000 – Injury crash Reporting requirements – Driver report “…is not required when the accident is investigated by a law enforcement agency.” Impact: May result in systematic change in apparent crash performance, e.g. crash reduction. 44

45. Reporting Extent & Completeness All public roads Private property State-maintained roads only Jurisdiction, agency dependent Impacts: Incomplete crash history skews findings. Difficult to compare different locations. 45

46. Multiple Data Sources Local law enforcement State DOT Other agencies, e.g. taxi authority Impact: Difficult to access and integrate all crash data, i.e. difficult to create a comprehensive, useable data set. 46

47. How Crash Data Are Abused 47

48. Limited Frame of Reference Limited, no comparison to similar locations. No comparison to “expected” conditions (comparables). Impact: What may appear to be a problem site, in isolation, may be performing as well as, or better than, similar locations. – However, this does not imply that a location is performing well and/or can not be improved. 48

49. Limited Perspective Decisions made, almost exclusively, based on crash history. Little consideration given to changes during analysis period… – Land use and development – Infrastructure – Traffic patterns – Other, e.g. construction during an analysis year Impact: Factors significantly impacting crash history are ignored. Solution no longer fits the problem 49

50. Regression to the Mean Crashes are random. Extreme conditions will generally return to “normal” state. Source: Safe Speed Impact: Overestimates effectiveness of treatment; focus on the wrong sites (should use EB or at least more data) 50

51. Analysis Period Shortcomings Limited (short) analysis period “Dated” crash data Impacts: May not accurately represent the performance of a site. Similar to regression to the mean. May not accurately reflect the existing conditions. 51

52. General Crash Data Issues Change in crash form Cartographic (base map) changes Location accuracy Change in statute Reporting extent & completeness Multiple data sources Impact: Not being aware of these issues – is it your responsibility? 52

53. Over-Emphasis on Single Crash Metric Frequency (total or by collision type) Rate Severity Impacts: focus on “glass and paint” rather than “human impact”; or “chasing fatals” 53

54. Problem 2: Fix This Mess Lincoln Way, Ames, Iowa 54

55. Data On Your Tables … 1.Complete set of data 2.25 meter buffer vs. “Functional area” 3.Crash frequency only vs. AADT and crash type 4.1 year of data vs. 10 years of data 5.Older data vs. recent data 6.Current aerial photo only vs. past development trend and detailed land use data 55

56. Workshop Wrap-Up What are some of the new things you learned today? Did the workshop meet your expectations? Do you think you will be able to apply what you learned in your work once the conference is over? David Plazak dplazak@nas.edu (just a guess)dplazak@nas.edu Zach Hans zhans@iastate.eduzhans@iastate.edu 56