1. CARE After 20 Years: Impact on Highway Safety Allen Parrish David Brown CARE Research & Development Laboratory 28 th International Forum on Traffic Records and Highway Information Systems

2. 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

3. CARE Research 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

4. Current 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)

5. CARE 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)

6. CARE 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

7. 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

9. FilteringFiltering 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

11. What Frequencies Tell You How Reliable Your Data Is Identification of Potential Problems –First cut –Approximate What Further Questions to Ask

12. 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

13. Cross-Tabs Give Some Answers DUI Examples: Times may differ by day of week Severity might differ by urbanization Many variables differ by age group

17. IMPACT Example Subject: DUI Countermeasures Questions: Which counties should receive emphasis for DUI countermeasures?

19. Why 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

21. IMPACT 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.

23. Another 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.

24. Young DUI Drivers All DUI Drivers

25. Another 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.

27. A More General Information Mining Example Subject: Older Drivers Most General Question: What are the crash differences in general between age 65+ drivers and their younger counterparts? IMPACT: Information mining over all relevant variables.

29. IMPACT 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?

31. IMPACT’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

33. CARE Implementation Approach We take your source data: –Oracle –DB2 –SQL Server –Access –Excel –IMS –Flat files

34. Implementation 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

35. Basic Structure ODBC Data Source Custom middleware CARE Data Set Analysis Tools Oracle, SQL Server, Access CARE

36. Other 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

37. CARE Critical Analysis Reporting Environment A Paradigm for Information Mining http://care.cs.ua.edu http://www.info-mining.com