Using data mining to improve traffic safety programs

Scott Solomon (Graduate Division of Business and Management, Department of Information Technology, Johns Hopkins University, Rockville, Maryland, USA)
Hang Nguyen (Graduate Division of Business and Management, Department of Information Technology, Johns Hopkins University, Rockville, Maryland, USA)
Jay Liebowitz (Graduate Division of Business and Management, Department of Information Technology, Johns Hopkins University, Rockville, Maryland, USA)
William Agresti (Graduate Division of Business and Management, Department of Information Technology, Johns Hopkins University, Rockville, Maryland, USA)

Industrial Management & Data Systems

ISSN: 0263-5577

Publication date: 1 June 2006

Abstract

Purpose

The purpose of this paper is to demonstrate how the use of data mining (DM) analysis can be used to evaluate how well cameras that monitor red‐light‐signal controlled intersections improve traffic safety by reducing fatalities.

Design/methodology/approach

The paper demonstrates several different data modeling techniques – decision trees, neural networks, market‐basket analysis and K‐means models. Decision trees build rule sets that can abet future decision making. Neural networks try to predict future outcomes by looking at the effects of historical inputs. Market‐basket analysis shows the strength of the relationships between variables. K‐means models weigh the impact of homogenous clusters on target variables. All of these models are demonstrated using real data gathered by the Department of Transportation from fatal accidents at red‐light‐signal controlled intersections in Maryland and Washington, DC from the year 2000 through 2003.

Findings

The results of the DM analysis will show predictable relationships between the demographic data of drivers and fatal accidents; the type of collision and fatal accidents and between the time of day and fatal accidents.

Research limitations/implications

The limitations of missing or incomplete data sets are addressed in this paper.

Practical implications

This paper can act as a guide to follow for red light camera program managers or local municipalities to conduct their own analysis.

Originality/value

This paper builds upon prior research in DM and also extends the body of research that examines the effectiveness of red camera programs as they mature.

Keywords

Citation

Solomon, S., Nguyen, H., Liebowitz, J. and Agresti, W. (2006), "Using data mining to improve traffic safety programs", Industrial Management & Data Systems, Vol. 106 No. 5, pp. 621-643. https://doi.org/10.1108/02635570610666412

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Publisher

:

Emerald Group Publishing Limited

Copyright © 2006, Emerald Group Publishing Limited

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