CARE After 20 Years: CARE After 20 Years:

Impact on Highway SafetyImpact on Highway Safety

Allen ParrishDavid Brown

CARE Research & Development Laboratory

28th International Forum on Traffic Records and Highway Information Systems

Saving Lives Since 1982…Saving Lives Since 1982…Saving Lives Since 1982…Saving Lives Since 1982…

• We are 20 years old!• Historical summary:

– CARE: Originally a front end to SPSS

– Evolved to a separate Windows-based product

– Basic analysis functionality also over the Web

– Location analysis and roadway geometrics

– Won NHTSA Administrator’s award in 1995

– Current or past contracts with six states

CARE CARE Research and Development LaboratoryResearch and Development LaboratoryCARE CARE Research and Development LaboratoryResearch and Development Laboratory

• Organization of about 20 people:– Faculty, professional staff (mostly developers), students

• CARE is our principal product– Data analysis software– Worked with AL, FL, IA, MI, NC, TN

• We are also supporting data collection:– Crash data (electronic forms)– Citations, police incident reports– Laptop and PDA-based collection components

• Produce planning documents, reports and studies:– Alabama Crash Facts Book– Alabama Highway Safety Plan– Special studies

Current SponsorsCurrent SponsorsCurrent SponsorsCurrent Sponsors

• Alabama Departments of – Transportation

– Economic and Community Affairs (GR-TSO)

– Administrative Office of Courts

– Public Safety

• SW Ala. Integrated Criminal Justice System• North Carolina, Tennessee, Iowa, and Florida• LESIS (State Office)

CARE SoftwareCARE SoftwareCARE SoftwareCARE Software

• Allows access to crash data• Several analysis techniques:

– Frequency distributions– Cross-tabulations– Crash rates per population size (ACT)– Location analysis (variety of techniques

here)

• Information Mining (IMPACT)

CARECARE Goal: Information Generation Goal: Information GenerationCARECARE Goal: Information Generation Goal: Information Generation

• Latent Information in Crash Database– Extremely valuable and useful– Going to waste– Sophisticated databases create walls– Complex coding and retrieval methods

• Need Tools to Produce Information– Data retrieval– Information generation

Information Generation Starts by…Information Generation Starts by…Information Generation Starts by…Information Generation Starts by…

• Getting Familiar with Your Data• What is in the Database?

– The variables– Their possible values

• Is it Valid?– How many missing cases?– How many unknowns?

• Simple Frequency Distributions

FilteringFilteringFilteringFiltering

• The Ability to Consider Subsets• Examples: Alcohol, Bicycle, Pedestrian

• Total Flexibility in Filter Creation– Create general (simple) filters

– Combine for any degree of complexity

• Select Filter; Choose Analysis

What Frequencies Tell YouWhat Frequencies Tell YouWhat Frequencies Tell YouWhat Frequencies Tell You

• How Reliable Your Data Is

• Identification of Potential Problems– First cut– Approximate

• What Further Questions to Ask

But Raw Frequencies …But Raw Frequencies …But Raw Frequencies …But Raw Frequencies …

• Generally Do Not Produce Information

• Generate More Questions than Answers

• Basically Data, Not Information

• To Become Information – They must be compared to something else– Demographic or internal comparison

Cross-Tabs Give Some AnswersCross-Tabs Give Some AnswersCross-Tabs Give Some AnswersCross-Tabs Give Some Answers

DUI Examples:

• Times may differ by day of week• Severity might differ by urbanization• Many variables differ by age group

IMPACT ExampleIMPACT ExampleIMPACT ExampleIMPACT Example

Subject: DUI Countermeasures

• Questions:Which counties should receive emphasis for DUI countermeasures?

Why Comparison is EssentialWhy Comparison is EssentialWhy Comparison is EssentialWhy Comparison is Essential

• Most Alcohol Crashes in Mecklenburg– Mecklenburg County has most population– It has most of all crash types– Should it get all the funding?

• Must Compare Rates • Must Have “Denominator”• This Can be Done by Frequency Analysis

– Compares subset against complement– In this case: Alcohol against Non-Alcohol

IMPACT Takes it One Step FurtherIMPACT Takes it One Step FurtherIMPACT Takes it One Step FurtherIMPACT Takes it One Step Further

• Pinpoints Where Problems Are• Counties with Potential Alcohol Problems

– Buncombe County (only 402 alcohol crashes)

– Substantially over-represented for alcohol

– Other mid-sized counties show same pattern

• IMPACT: seeks maximum potential return• Similar analysis for city, time, age etc.

Another IMPACT ExampleAnother IMPACT ExampleAnother IMPACT ExampleAnother IMPACT Example

Subject: Young Driver Alcohol

• Question:

How do we adjust time of day selective

enforcement for younger DUI drivers?

• IMPACT:Compare young drivers against their

older DUI counterparts.

Young DUI Drivers All DUI Drivers

Another IMPACT ExampleAnother IMPACT ExampleAnother IMPACT ExampleAnother IMPACT Example

Subject: Older Drivers

• Question:To what extent should we address night-time signing for senior citizens?

• IMPACT: Compare senior citizens time of day against times for younger drivers.

A More General A More General Information Mining ExampleInformation Mining Example

A More General A More General Information Mining ExampleInformation Mining Example

Subject: Older Drivers

• Most General Question:What are the crash differences in general between age 65+ drivers and their youngercounterparts?

• IMPACT: Information mining over all relevant variables.

IMPACT Example RevisitedIMPACT Example RevisitedIMPACT Example RevisitedIMPACT Example Revisited

Subject: Older Drivers

• Question:To what extent should we address night-time signing for senior citizens?

• IMPACT: What are the factors involved in senior citizen crashes at night?

IMPACT’s Instant DrilldownIMPACT’s Instant DrilldownIMPACT’s Instant DrilldownIMPACT’s Instant Drilldown

• Click on Any Cell of IMPACT Output• Perform Instant Analysis of Issue

– More detailed IMPACT analysis– Compares crashes in that cell against all in

subset– Here: dark roadway older driver crashes

against all older driver crashes

• Who – What – Where – When - Why

CARE Implementation ApproachCARE Implementation ApproachCARE Implementation ApproachCARE Implementation Approach

• We take your source data:– Oracle– DB2– SQL Server– Access– Excel– IMS– Flat files

Implementation ApproachImplementation ApproachImplementation ApproachImplementation Approach

• We provide a middleware solution:– Data is imported into CARE format from the source database– Driver is provided to support continuous connectivity between

the source database and the CARE local data– A local copy of the data is maintained on your desktop system

or server to facilitate speed of access

• Continuous update capability– Server-side tools maintain currency of data on your desktop– Each state has a Web site to manage their data, and to provide

analysis and download capability

Basic StructureBasic StructureBasic StructureBasic Structure

ODBCData

Source

Custommiddleware

CAREDataSet

Analysis Tools

Oracle, SQL Server,Access

CARE

Other Relevant ProjectsOther Relevant ProjectsOther Relevant ProjectsOther Relevant Projects

• ACES: Alabama Crash Entry System– Laptop-based crash form entry system

• WISE: WIreless Simultaneous Entry– PDA-based data capture

– PDA contains integrated scanner

– Wireless transmission back to in-car laptop

– Customizable to any form

– We are currently working to obtain TraCS compatibility

CARECARECritical Analysis Reporting EnvironmentCritical Analysis Reporting Environment

CARECARECritical Analysis Reporting EnvironmentCritical Analysis Reporting Environment

A Paradigm for Information Mining

http://care.cs.ua.edu

http://www.info-mining.com