{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T23:30:38Z","timestamp":1715211038139},"reference-count":43,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2006,6,1]],"date-time":"2006-06-01T00:00:00Z","timestamp":1149120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006,6,1]]},"abstract":"Purpose<\/jats:title>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\u2010light\u2010signal controlled intersections improve traffic safety by reducing fatalities.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>The paper demonstrates several different data modeling techniques \u2013 decision trees, neural networks, market\u2010basket analysis and K\u2010means 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\u2010basket analysis shows the strength of the relationships between variables. K\u2010means 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\u2010light\u2010signal controlled intersections in Maryland and Washington, DC from the year 2000 through 2003.<\/jats:p><\/jats:sec>Findings<\/jats:title>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.<\/jats:p><\/jats:sec>Research limitations\/implications<\/jats:title>The limitations of missing or incomplete data sets are addressed in this paper.<\/jats:p><\/jats:sec>Practical implications<\/jats:title>This paper can act as a guide to follow for red light camera program managers or local municipalities to conduct their own analysis.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/02635570610666412","type":"journal-article","created":{"date-parts":[[2006,7,4]],"date-time":"2006-07-04T04:24:45Z","timestamp":1151987085000},"page":"621-643","source":"Crossref","is-referenced-by-count":24,"title":["Using data mining to improve traffic safety programs"],"prefix":"10.1108","volume":"106","author":[{"given":"Scott","family":"Solomon","sequence":"first","affiliation":[]},{"given":"Hang","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Jay","family":"Liebowitz","sequence":"additional","affiliation":[]},{"given":"William","family":"Agresti","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022012720120776300_b1","doi-asserted-by":"crossref","unstructured":"Aiken, M. 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