A Host Architecture for Automobile License Plate Recognition

• May 1st, 2008
• in

The objective of this paper was to present an architecture that supports data transmission and data sharing among applications related to commodity tag recognition systems. These systems are used extensively in Great Britain and other countries to recognize automobile license plates and to notify security officials of suspicious activity or trafficking by known suspects. This architecture was tested in a successful pilot project.

Identifying High Frequency Crash Locations: Empowering End-Users with GIS Capabilities

• January 1st, 2007
• in

This paper presented the status of CARE with emphasis on its newly created GIS capabilities as of January, 2007. Emphasis is placed on the following aspects of the CARE/GIS system:

• The timeliness of the data and the use of temporal analysis to track changes over time;
• The ability to create filters using GIS and use them within the CARE analytical engines;
• The integration of demographic GIS layers for schools, hospitals, bridges, census data; roadway inventory, railroads, etc.;
• The extension of the desktop system to a Web-based system.

Several additional innovations have been made to CARE since early 2007, and those interested are urged to review the CARE pages within this site.

Improved Variable and Value Ranking Techniques for Mining Categorical Traffic Accident Data

• December 1st, 2005
• in

This paper reviews the use of two new metrics for the process of assessing the significance of attributes in a database when two subsets of the data are compared. Traditional statistical techniques are useful, and the sample size in public safety databases usually allows the normal approximation to the binomial distribution to be used in comparing proportionate values. For example, the comparison of the proportion of alcohol related crashes on Saturdays would show an very highly significantly higher proportion than that for non-alcohol related crashes. However the new metrics go a step further than this in that they provide a clear intuitive grasp to the user as to exactly how much more is occurring, not in terms of proportions but in terms of number of crashes (for the traffic safety example). The metric is called Maximum Gain, and it measures directly the number of crashes over and above that which is typically expected. This provides a clear indication to the user of just what the potential gain is by applying a countermeasure related to the attribute (e.g., applying selective enforcement on Saturdays). It is not realistic to think that this gain would include all of the crashes for the attribute value; rather, it is realistic to view the maximum gain to be the total over-represented amount.

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.