Use and Abuse of Crash Data in Roadway Access Management

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

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

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

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

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

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

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

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

11. Conflict Points Diverging Merging Crossing

12. Examples of Conflict Point Reduction Treatments

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

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

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”

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%

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%

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%

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

20. What Do Crash Data Really Look Like?

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

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

23. An Example Plan …

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

25. Break

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

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

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 …

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

30. The Characteristics of Data Quality (The “Six-Pack”)

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!

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

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

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

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)

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

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

39. Changes in Crash Forms Collision Type Before After • Content • Addition/elimination of attributes collected • Change in definitions (values)

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

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

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

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

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.

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.

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.

47. How Crash Data Are Abused

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.

49. Limited Perspective • Decisions made, almost exclusively, based on crash history. • Little consideration given to changes during analysisperiod… • 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

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