1. Crash Data Collection and Quality

2. Why collect/maintain safety data? Khisty says: Is that all? – Better understanding of operational problems – Accurate diagnosis of crash problems – Develop remedial measures – Evaluate the effectiveness of road safety programs

3. Who uses crash data? – Road safety engineers Develop remedial measures – Police Charging a person at fault in crash Enforcement activities – Location of speed cameras – Breath testing stations – Insurers Seeking facts before settling claims – Lawyers Compensation for injuries – Road safety educators To ensure that their efforts well targeted – Safety administrators Report statistical information on road crashes – Researchers Access good reliable database – Vehicle manufacturers Assess the safety of their products Importance of good data (Video “L”)

5. And, for Commercial Motor Carriers … Identifying the appropriate Commercial Motor Carrier Determining Reportable Crashes Identifying Vehicle Configuration and Cargo Body Type Determining Sequence of Events Recording Hazardous Materials Recording proper CDL

6. Supplementary data sources (Ogden) While police crash report is the basic source of crash data, there are some other sources which may be useful and applicable in certain circumstances – Local knowledge Local government staff Emergency service personnel Local safety groups Local businesses – Interview of road users People involved in a crash at a site of interest, which are source of useful information for traffic officials in development of countermeasures – In-depth studies of particular group of crashes Single vehicle fatal crashes, to gain better understanding of the nature of those crashes

7. Supplementary data sources (continued) – Traffic conflict surveys May be used when the collection of crash data is not practical or period of evaluation is too short to collect sufficient samples – Field observation – Video recording of conflicts Information gained in this way is valuable in – getting a sound understanding of the traffic operation – Find interactions between traffic streams at the site As a proxy measure of safety – Assumption must be made about relationship between proxy measure (conflict) and crash rates – Site investigations are necessary component of a countermeasure development program

8. What is reportable? In most US states, the five point scale often referred to as KABCO – K person with fatal injury – A person with incapacitating injury – B person with non-incapacitating evident injury – C person with possible injury – O no injury (property damage only) Some countries report injury crashes only Some states do not differentiate between injury types – Implication? Some crashes are not reported … why? Many states use a reporting “threshold” – May vary even within states … implication?

9. Impact of threshold adjustments

12. Sketch and narrative

13. Collision Diagrams

15. http://www.nhtsa-tsis.net/stateCatalog/stateData.html

16. In-class exercise Crash form elements and the Haddon Matrix

21. Storage/retrieval <500 annually may be filed (paper) with summary tables Increasingly, all data are input into a database (and forms scanned) Feeds state and national databases

22. Old Location Process

23. Data Collection Technologies TraCS: Traffic and Criminal Software

24. TraCS data entry form

25. Incident Location Tool (and IMAT)

26. Easy Street Draw & Visio Florida TraCS show

28. Case study – access management From …

29. Use and Abuse of Crash Data in Roadway Access Management A Workshop at the National Access Management Conference Baltimore, Maryland July 13, 2008 Case study – access management From …

30. Data-Driven Access Management Access management treatments and plans should be directly tied to measurable objectives such as crash 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 30

31. 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 31

32. Problem 1: Fix This Mess South Ankeny Blvd., Ankeny, Iowa 32

33. What Do Crash Data Really Look Like? 33

34. What’s On Your Table … 34 Land Use Crash data tables and charts Crash data stack mapLaminated base map Traffic over time Corridor photos 34

35. An Example Plan … 35

36. 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 … 36

37. You Need Good Quality Data The Ingredients Matter: Quality Control 37

38. The Characteristics of Data Quality (The “Six- Pack”) 38 Data AccessibilityIntegration Consistency Uniformity CompletenessAccuracy Timeliness

39. FMCSA Data Quality

40. 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! 40

41. Crash Data Quality: Accuracy Spatial Location Attributes, e.g., severity, crash type, roadway info 41 SOUTH ANKENY BOULEVARD 1 ST Road v Original Considering functional area

42. 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 Random bias - Under-reporting can result in distorted picture of road crash situation 42 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%

43. Crash Data Quality: Consistency/Uniformity Across jurisdictions Across time Consistent severities Discontinuities – Data from one time period can not be compared to another time period 43

44. Crash Vehicle Person Roadway MMUCC and MIRE Model Minimum Uniform Crash Criteria Model Inventory of Roadway Elements

45. Consistency Although the need for data is universally recognized, there is little consistency in collected data (Ogden) – Comparative study of eleven European countries found that Only two variables (date & hour) were collected in all eleven countries 7 percent of items were recorded in three countries 70 percent recorded in only one country – There is no nationwide crash data reporting system in US Little consistency within states for recorded data elements

46. 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) Privacy is a tough issue Another tough issue is multiple offices and even agencies being in charge of various parts of safety data 46

47. Crash Data Quality: Accessibility How can you get crash data? How easy is it to get? What form do you want it in? Liability and perception is an issue. Continuum: not available … special request w/delay … regular updates … service … instant web access 47

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

49. 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 49

50. 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 50

51. Cartographic (Base Map) Changes Shift, update to reference road network Impact: Challenging to systematically assign crash location. 51

52. 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 52

53. 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 53

54. 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. 54

55. 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. 55

56. 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. 56

57. How Crash Data Are Abused 57

58. 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. 58

59. 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 59

60. 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) 60

61. 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. 61

62. 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? 62

63. Problem 2: Fix This Mess Lincoln Way, Ames, Iowa 63

64. 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 64

65. Locational Challenges for Next Generation of Crash Data Systems

66. SAFETEA-LU Section 1401 (Highway Safety Improvement Program) ID of top 5% of public hazardous locations on all roads

67. Local Road GIS Data Where some states are now

68. Inventory data on all roads? The “quadrennial needs” legacy Yes Some, quality issue, or working on it No No Response State system as a percent of all public roads

69. Can 1401 be met without GIS? Kansas, for example … Has crashes on system only Has ≈ 70% of crashes located to road by route milepost Does sliding spot (nongraphical) & “named intersection” (program) Assuming the 30% missing does not affect the outcome No brainer to do top 5%

70. Location An early computerized “spot” map (from Khisty) Can you “spot” the problems?

71. Other examples

72. Crashes by Time of Day

73. Crashes by Age

74. Crashes by Road Surface Conditions

75. Drug and Alcohol Related Crashes

76. GIS-ALAS: Corridor Crash Frequency (stacked)

77. Injury Frequency by Severity

80. Mason City Waterloo Cedar Rapids Quad Cities Des Moines Council Bluffs Iowa City Ames Sioux City Dubuque Fort Dodge Ottumwa Marshalltown Spencer Clinton 1 yr of data Crash Density – 1 Year Average Annual Fatal and Major Injury Crashes Per Mile Sample - DRAFT

81. Mason City Waterloo Cedar Rapids Quad Cities Des Moines Council Bluffs Iowa City Ames Sioux City Dubuque Fort Dodge Ottumwa Marshalltown Spencer Clinton 3 yrs of data Crash Density – 3 Year Average Annual Fatal and Major Injury Crashes Per Mile Sample - DRAFT

82. Mason City Waterloo Cedar Rapids Quad Cities Des Moines Council Bluffs Iowa City Ames Sioux City Dubuque Fort Dodge Ottumwa Marshalltown Spencer Clinton 5 yrs of data Crash Density – 5 Year Average Annual Fatal and Major Injury Crashes Per Mile Sample - DRAFT

83. Mason City Waterloo Cedar Rapids Quad Cities Des Moines Council Bluffs Iowa City Ames Sioux City Dubuque Fort Dodge Ottumwa Marshalltown Spencer Clinton 10 yrs of data Crash Density – 10 Year Average Annual Fatal and Major Injury Crashes Per Mile Sample - DRAFT

84. Location methods address offset from known point (intersection, bridge, crossing, milepost) Literal description Smart map Lat/long or other coordinates (GPS) Aerial photo Multiple methods required Spatial analysis methods Spot/Intersection Analysis Strip Analysis Cluster Analysis Sliding-Scale Analysis Corridor Analysis Spatial statistics is an emerging area But …some technical issues

85. Some not-so “simple” questions

86. Feature not represented Feature under construction Alignment OK Alignment Off Where are the roads? (Incorrect or incomplete cartography)

87. Where are the roads? (Improving cartography) Alignment moves Alignment stays put

88. Where are the crashes? Crashes are not necessarily point events Some crashes may be located using different methods and degree of accuracy – Temporal (e.g. link node to lat long) – Spatial (e.g., state police v. local) – Techno (GPS v. smart map) ?

89. What’s “the” traffic volume on “the” road? Need traffic level for the year the crash happened Requires multiple files – in Iowa, working on going back past 1998 – difficult to do Was the road even there then? Is the road still there?

90. How to segment the road system? Requirements – Logical breaks (engineering and public) – Relationship to inventory data – Long enough for manageability and presentation – Short enough to reflect important changes – Clear and understandable to use Facility location and type – What is rural/urban? Character is important … Designated “rural”

91. Can use attributes and/or proximity… Red: probable, Yellow: spatial @ 75’, Blue: possible + spatial What is an intersection crash?