Spatial Distribution of Alcohol Involved Crashes in Louisiana

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Presentation transcript:

Spatial Distribution of Alcohol Involved Crashes in Louisiana Jeff Dickey, Ph.D. LSU Highway Safety Research Group 2017 ATSIP Traffic Records Forum

Highway Safety Research Group Funded by Louisiana Department of Transportation and Development Research group within the Stephenson Department of Entrepreneurship & Information Systems (SDEIS) at LSU Developed LaCrash application to electronically capture crash reporting information Collect, correct and analyze crash data for the state and other stakeholders

Research and Support Seat belt use Drugged driving Roadway departure Crash map spotting Crash hot spot mapping for regional safety coalitions In depth hot spot analysis

Alcohol Involved Crashes Alcohol-impaired driving accounts for 31% of all traffic-related deaths in the USA – CDC Half of Youth Car Crash Deaths Involve Alcohol -medpagetoday.com/pediatrics February 13, 2017 Every 2 minutes, a person is injured in a drunk driving crash – MADD In Louisiana, 296 of the 758 total fatalities in 2016 were alcohol- involved crashes (39.05%) -LSU HSRG

Spatial Analysis of Alcohol Involved Crashes Use driver’s home zip code Drinking and driving as a risky behavior Cultural norms “Laissez les bon temps rouler” “Left of boom”

Data for Spatial Analysis All moderate injury, severe injury, and fatal crashes on local and state roads for 2005-2014 100,868 Louisiana drivers in alcohol involved crashes 82,827 records with valid driver zip code 4,991 recorded alcohol – 6.0% 13,204 predicted alcohol – 15.7%

Data for Spatial Analysis Import crash records into ArcMap Create points with ZCTA latitude and longitude Spatial join to ZCTA to sum recorded and predicted alcohol Merge ZCTA with < 10 crashes into surrounding or adjacent ZCTA Calculate recorded alcohol crash rate and predicted alcohol crash rate

Tools for Spatial Analysis ESRI ArcMap 10.4 Spatial Statistics Tools Mapping Clusters Optimized Hot Spot Analysis (Getis-Ord Gi*)

Recorded Alcohol Crash Rate

Correlation of Recorded Alcohol Crash Rate

Predicted Alcohol Crash – hour, day of week Driver – alcohol, condition, age, sex, protection system, violations Pedestrian – alcohol, condition Vehicle type, number of vehicles, parish code

Predicted Alcohol Crash Rate

Correlation of Predicted Alcohol Crash Rate

Correlation of Difference Predicted minus Recorded Alcohol Crash Rate

Interesting Findings Predicted alcohol generally higher across the state Highest recorded and predicted alcohol in the Houma area Alcohol involvement appears to be under reported in north Louisiana

Future Research Incorporate socioeconomic data from American Community Survey Administer survey of behavior and perceptions of risk Examine assumptions underlying the predicted alcohol algorithm

Questions & Discussion Thank you! Jeff Dickey, Ph.D. jdickey@lsu.edu