Unifying Alabama's Traffic Safety Efforts
     Working Together to Save Lives    
The Critical Analysis Reporting Environment (CARE) is a data analysis software package originally designed for problem identification and countermeasure development in traffic safety applications. Developed by the staff of the Center for Advanced Public Safety, CARE uses advanced analytical and statistical techniques to generate valuable information directly from the data. Although its primary use is still in traffic safety areas, CARE can be used to process any database, and its most recent applications have included databases in the areas of emergency medical services, medical, nursing data, a variety of questionnaires, and several criminal justice applications.

In 2003 the electronic citation (eCite) system was introduced in the state of Alabama, beginning with the Heflin weigh station. This system allows officers to utilize a license scanner, GPS device and laptop to write traffic citations quickly and easily from their vehicles. By 2007 this program was deployed to every state trooper, and it is now being rolled out to other law enforcement agencies throughout the state.

To view the CARE Dashboard, Click Here

For more information about eCite, Click Here

Most of your statistical information needs can be obtained right on line from the new CARE Dashboard, which was developed by the University of Alabama Center for Advanced Public Safety (CAPS).  Before getting on this site, please take the tutorials given on the CAPS page, which will explain the data and use of CARE and the Dashboard. If you would like to see details on the CARE software, it is available on the CARE page of the CAPS website. If you see any problem or need help, e-mail care@cs.ua.edu or call 205-348-7920.
Updated Model Police Crash Report Includes Autonomous Vehicles
August 1, 2017

Contact: Madison Forker, mforker@ghsa.org
States Urged to Utilize Updated Traffic Crash Guideline for Better Data Collection and Reporting
Model crash report, context-specific sections, and a new element for autonomous vehicles will make it easier for states to collect and share data to improve highway safety
WASHINGTON, D.C. - With traffic fatalities on the rise, guidance on how to collect high-quality crash data becomes ever more important. Developed cooperatively by the Governors Highway Safety Association (GHSA) and the National Highway Traffic Safety Administration (NHTSA), the Model Minimum Uniform Crash Criteria (MMUCC) 5th Edition guideline has been updated to reflect the latest behavioral and technological changes impacting vehicles, drivers, and front-line data collectors.
The voluntary MMUCC guideline identifies motor vehicle crash data elements and attributes that states are encouraged to collect and include in their crash data systems. It encourages greater uniformity and common definitions for vehicle crash data to make it easier to share and compare data at the local, state and national levels. This most recent update streamlines on-site data collection, reorganizing the guideline into context-specific modules related to the type of crash and allowing more data to be derived from the integrated systems many states now use.
Quality crash data is vital to safety on our nation's roadways. It is used to identify critical issues, shape highway safety messages, target enforcement efforts to the areas that need them most, inform the development of needed highway safety legislation, and evaluate the impact of highway safety countermeasures.
"So much is changing on our roadways, and traffic fatalities are increasing at an alarming rate," said Jonathan Adkins, GHSA Executive Director. "We need good data to make informed decisions about how to change driver behaviors and save more lives. GHSA strongly encourages states to align their crash records with MMUCC and collect comprehensive, consistent data that is critical to pinpointing regional and national trends."
Other important updates to the MMUCC 5th Edition include guidance to help improve data quality, mapping rules to help states measure the extent to which their crash data aligns with MMUCC, and an editable MMUCC-based crash report template. A dynamic element that captures data on crashes involving autonomous vehicles (AVs) has also been introduced. To keep pace with rapid development in this area, the AV element will be reviewed on an annual basis.
The MMUCC 5th Edition is the result of 18-month collaboration between NHTSA, GHSA, the Federal Highway Administration (FHWA), the Federal Motor Carrier Safety Administration (FMCSA), the National Transportation Safety Board (NTSB), and subject matter experts from state Departments of Transportation (DOTs), local law enforcement, emergency medical services, safety organizations, industry partners, and academia. The traffic records community and general public also contributed comments. The next update of MMUCC is tentatively scheduled for 2022.
eCrash System Deployed
The Alabama Department of Public Safety teamed with the University of Alabama Center for Advanced Public Safety (CAPS) to develop eCrash, the nation’s first totally paperless crash reporting system. Except for the reports provided to those involved in crashes, all other aspects of eCrash are paperless, from the officers’ entry of the data (in many cases in their vehicles), through the approval process and uploading to the crash records database in Montgomery. The use of eCrash for data entry in the officers' vehicles will keep them in the field where they can respond to emergencies; and moving the data entry to the field will make the data more accurate, timely, complete and consistent. About 95% of crash reporting agencies either use eCrash or submit their crash records electronically in eCrash format, which is now MMUCC compliant. The target date to get all agencies using the eCrash report is December 31, 2010.
Description of CARE Impact Output
The following IMPACT that compares Alcohol Crashes (red bars) vs. Non-Alcohol (blue bars) for the Day of the Week variable will be used to explain IMPACT outputs.

The above example IMPACT output for the alcohol related day of the week variable will be used for this explanation. The left (red) bars represent the current subset (Alcohol Related/DUI Crashes for this example), while the right (blue) bars represent the proportion of crashes that are in the subset being compared (in this case the complement, which is all crashes for which non-DUI causation was reported).

Note the largest Max Gain first output lists the “worst first” – all other things being equal. The first two numeric columns of the chart give the frequency and percentage for the subset (as defined by the current filter). In this case the subset is alcohol related crashes. So, Alabama had 1,718 alcohol crashes on Saturday for this particular time period, which were 25.478% of its total alcohol crashes. This is compared against the “Other” subset, which in this case is the non-alcohol crashes. This indicates that 14,650 non-alcohol crashes occurred on Saturday, which is 12.497% of all non-alcohol crashes.

To determine if there is an over-representation of alcohol crashes on Saturday, the proportions must be compared. In this example, 25.478/12.497 = 2.039, which is to say that there are more than twice the number of crashes than would typically be expected if Saturday alcohol crashes were the same as Saturday non-alcohol crashes. Note that the double bar chart for Saturday shows the same thing visually. A statistical test is performed, and the asterisk (*) on the 2.039* indicates that this is significant at the 99% level according to a statistical t-test that assumes a normal approximation of the binomial distribution. Statistical tests are not performed (no asterisk will ever appear) if either of the sample sizes used to compute the proportions (percentages) are less than 20.

Finally, the Max Gain (Maximum Gain) is the number of crashes that would be saved if we could somehow eliminate the over-representation. The total number of these crashes is 875 – notice that this is over half of the 1,718 alcohol crashes that occurred on Saturday. All other things being equal, this over-representation gives a measure of the maximum gain that could be expected if we could eliminate the particular (in this case “Saturday”) problem.