Statistical Modeling for Intersection Decision Support
2006-02-01
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Statistical Modeling for Intersection Decision Support
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2006-02-01
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This project was a component of the Intersection Decision Support (IDS) effort conducted at the University of Minnesota. In this project, statistical modeling was applied to crash data from 198 two-way, stop-controlled, intersections on Minnesota rural expressways, in order to: (1) identify intersections that were plausible candidates for future IDS deployment; (2) develop a method for estimating the crash-reduction effect of IDS deployment; (3) develop a method for predicting the crash-reduction potential of IDS deployment, and (4) test the hypothesis that older drivers were over-represented in intersection crashes along US Trunk Highway 52. All these objectives were accomplished using hierarchical model structures similar to that employed in the Interactive Highway Safety Design Model. Five rural expressway intersections were identified as having crash frequencies that were atypically high, and this group included the intersection of US Trunk Highway 52 and Goodhue County highway 9, the site chosen for the prototype IDS deployment. It was then determined that a 3-year count of crashes after deployment would probably be sufficient to detect any crash reduction effect due to the IDS, although a reliable estimate of the magnitude of this effect would require a longer test period. Assuming that the effect of an IDS deployment would be to make the crash frequencies at treated intersections similar to that experienced by typical intersections, it was estimated that deployment of the IDS at the five high-crash intersections would, over a 15-year period, result in a reduction of about 308 crashes. Finally, using an induced-exposure approach, twelve intersections were identified as showing over-representation of older drivers, five of these being on US Trunk Highway 52.
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Minnesota Department of Transportation
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Davis, Gary A.; Tilahun, Nebiyou; Mesa, Paula. (2006). Statistical Modeling for Intersection Decision Support. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/1021.
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