Surface transportation in the U.S. is continuously affected by adverse weather conditions, which contribute to thousands of highway fatalities and billions in economic loss every year. Winter weather has its own peculiar challenges, to which transportation agencies respond through winter maintenance operations. Recent trends toward proactive winter maintenance operations have placed a premium on the value of timely and accurate weather information that reflects current and forecast conditions in the roadway environment. However, few studies have sought to quantify this value, and compare it to the costs of obtaining such customized weather information. Motivated by the 2002 Winter Olympics, the Utah Department of Transportation (UDOT) implemented a Weather Operations Program which assists the DOT operations, maintenance, and construction functions by providing detailed, often customized, area-specific weather forecasts. This programs genesis provides a useful case study to quantify the cost-effectiveness of weather information. This paper describes the application of an artificial neural network model using winter maintenance cost data from dozens of UDOT maintenance sheds for the 2004-05 winter to estimate the cost-effectiveness of this program. The model estimated the value and additional saving potential of the UDOT weather service to be 11-25 percent and 4-10 percent of the UDOT labor and materials cost for winter maintenance, respectively. Based on the programs cost, the benefit-cost ratio was calculated at over 11:1. The results highlight the potential benefits that may be realized by an agency using improved weather information to direct its winter maintenance activities.
Samenvatting