Variable Selection and Ranking for Analyzing Automobile Traffic Accident Data

• April 1st, 2005
• in

This paper explores a data mining process in which the original dataset is first transformed through a variable subset selection process followed by the application of a machine learning algorithm. A variable ranking technique, called the Sum of Maximum Gain Ratio (SMGR), is applied. This technique computes a score that is based on the over-representation of attribute values. Essentially, SMGR is the ratio of the number of cases that could potentially be reduced by an effective countermeasure to the total number of cases associated with the over-represented value. SMGR was shown empirically to provide comparable results to alternative techniques, but it had significantly improved runtime performance.

Automated Selection of Auto Crash Causes

• April 1st, 2004
• in

This research built on the foundation of the Critical Analysis Reporting Environment (CARE), which was developed at the University of Alabama to mine crash reports submitted by investigating officers in the field. The research extended CARE capabilities by developing neural network algorithms to automatically learn potentially problematic attributes over time. The system was piloted and tested using records from Walker County, Alabama.

CARE: An Automobile Crash Data Analysis Tool

• June 1st, 2003
• in

This paper presents an early (2003) review of CARE that was published in IEEE Computer, the flagship publication of the IEEE Computer Society. The major points made in the paper include:

• The causes for CARE’s early success were twofold: (1) its simplicity of use, enabling safety practitioners with basic computer literacy skills to easily obtain information from it with a minimum of training; and (2) its efficiency, providing virtual instantaneous presentation of results for even the largest of databases (several hundred thousand records).
• That CARE had been implemented in a number of states.
• That CARE had received the 1995 NHTSA Administrator’s Award for innovation.
• That other applications were being made of CARE in addition to highway safety, namely databases were being mined at the Federal Aviation Administration (FAA) and NASA.

Automated Selection of Automobile Crash Countermeasures

• March 1st, 2003
• in

This paper describes a neural network approach to automatically select crash countermeasures. The approach uses as a base the CARE IMPACT analysis that mines the crash database for the most critical attributes. However, while CARE bases its countermeasure selection on the “maximum gain” that can be obtained by eliminating attribute value over-representations, two additional algorithms were introduced to determine the most significant variables and to rank the attributes, namely: (1) a Euclidean distance approach, and (2) an ellipse distance approach. An evaluation function is constructed from the neural network learning, capturing the decision making strategy and then used continuously to select countermeasures.