01593858

Component

https://doi.org/10.3141/2555-06

The reduction of upstream end-of-queue (EOQ) crashes at work zone lane closures has been a focus of the Texas Department of Transportation, Austin, for several years. An ongoing widening effort on Interstate 35 through central Texas prompted officials to examine and implement technologies to mitigate the impacts of EOQ warning crashes. An EOQ warning system was established, which consisted of a highly portable work zone intelligent transportation system of easily deployable radar speed sensors linked to one or more portable changeable message signs and highly portable transverse rumble strips. The EOQ system was deployed upstream of nighttime lane closures, where queues were expected to develop. Although the sample sizes were relatively small, the trends did suggest that the systems were having a positive effect to reduce crashes. Overall, the EOQ warning system was estimated to have reduced crashes 44% from what they otherwise would have been if the system had not been used. The crashes that did occur were less severe, which was most likely because fewer of them were of the high-speed, rear-end-collision variety. With traditional societal crash cost values updated to 2014 dollars, the use of the EOQ warning system at nighttime lane closures reduced crash costs by $1.36 million over the analysis period. This figure equated to $6,313 in crash cost savings per night of deployment. Compared with the approximate costs of procurement and deployment of these systems, a break-even point was achieved after 95 to 190 nights of use.

01594662

16-3587

English

pp 46–52

2016

Transportation Research Record: Journal of the Transportation Research Board

9780309441193

Print

Figures (6) ; Maps; Photos; References (19) ; Tables (3)

Highway safety; Lane closure; Queuing; Traffic crashes; Warning signals; Work zone safety; Work zone traffic control; Work zones

Interstate 35; Texas Department of Transportation

Texas

Highways; Operations and Traffic Management; Safety and Human Factors

PRP, TRIS, TRB, ATRI

Jan 12 2016 5:33PM

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• What Role Do Precrash Driver Actions Play in Work Zone Crashes? Application of Hierarchical Models to Crash Data
• Work Zone Crash Cost Prediction with a Least Median Squares Linear Regression Model