Abstract:A method for deriving quantitative relationships between road slipperiness, traffic accident risk and winter road maintenance (WRM) activity is described. The method is also applied to data from an area in southern Sweden. If a specific type of road slipperiness represents a large accident risk despite high WRM activity it is important to increase public awareness during such periods. If the type of slipperiness represents a large accident risk but is accompanied by low WRM activity, it is also important to in… Show more
“…et al (1995) pointed out that weather conditions can play an important role in the accident-generation process, and Schandersson (1998) showed an increase in accident risk during snowfall. Norrman et al (2000) showed that accident risks varied considerably between the same slipperiness types used in this study. Since different types have a different impact on the road climate, they should be weighted accordingly.…”
Section: Planning Winter Maintenance and New Road Sectionsmentioning
“…et al (1995) pointed out that weather conditions can play an important role in the accident-generation process, and Schandersson (1998) showed an increase in accident risk during snowfall. Norrman et al (2000) showed that accident risks varied considerably between the same slipperiness types used in this study. Since different types have a different impact on the road climate, they should be weighted accordingly.…”
Section: Planning Winter Maintenance and New Road Sectionsmentioning
“…For instance, a study conducted by Forgas et al (2009) showed that people more easily remember "bad" weather. On the other hand, studies on driver behavior and cognition point to great problems related to drivers' lack of awareness of risks and perception of bad weather in the present, leading to increased accident frequency during the early winter season (e.g., Norman et al, 2000). Individuals' belief adjustment and revision of hypotheses based upon new evidence (Einhorn and Hogarth, 1985) is manifested as a "surprise effect" related to the number of days with "fair weather" conditions they experienced prior to severe events (Eisenberg, 2004).…”
Section: User Bias and Perception Of Weathermentioning
Introducing web weather 2.0, this paper suggests that active participation by civil society may arise through sharing of environmental data through observations of weather and other measurable variables in the environment performed by individuals. Collecting data from individuals is here suggested for improving weather data currently used by weather research centers and practitioners. Extending these current sets of weather data by using web 2.0 may address some issues stated by the World Meteorological Organization (WMO) regarding spatial and temporal resolutions of meteorological data including knowledge on different processes between the air and other environmental systems. To test the concept of web weather 2.0, the usability of weather data collected from individuals and the expected quantities of such data need to be determined. In addition, collection methods should be developed. Aiming at the design of an artifact that can meet these needs, this paper presents some important steps of the design process of a "share weather" system, including several demonstrations and experiments performed on different user groups, i.e. school children performing weather observations as a part of their daily tasks and education, and adults interested in weather due to their daily dependence on traffic conditions. This paper provides new knowledge about usergenerated observations of weather, including quality and motivation to contribute, and guidance on how future systems for collection of environmental data from individuals may be created. After testing the feasibility of the designed "share weather" artifact, we conclude that the potential role of individuals in producing valuable information beneficial to society should be considered within several branches of environmental sciences as well as policy-making.
“…The Washington State Department of Transportation estimated that between 1992 and 2004 Snoqualmie Pass on Interstate Highway 90 (I-90) was closed 120 hr per year on average, causing an annual loss of at least 17.5 million dollars. Ice and snow also increase the risk of accidents (Norrman, Eriksson, and Lindqvist 2000;Eriksson and Norrman 2001). The crash rate on the I-90 Mountains to Sound Greenway, Washington State's primary east-west bound highway, in the presence of snow is about five times the rate in clear conditions (Federal Highway Administration 2006).…”
Winter road maintenance is one of the main tasks for the Washington State Department of Transportation. Anti-icing, that is, the preemptive application of chemicals, is often used to keep the roadways free of ice. Given the preventive nature of anti-icing, accurate predictions of road ice are needed. Currently, anti-icing decisions are usually based on deterministic weather forecasts. However, the costs of the two kinds of errors are highly asymmetric because the cost of a road closure due to ice is much greater than that of taking anti-icing measures. As a result, probabilistic forecasts are needed to optimize decision making.We propose two methods for forecasting the probability of ice formation. Starting with deterministic numerical weather predictions, we model temperature and precipitation using distributions centered around the bias-corrected forecasts. This produces a joint predictive probability distribution of temperature and precipitation, which then yields the probability of ice formation, defined here as the occurrence of precipitation when the temperature is below freezing. The first method assumes that temperatures, as well as precipitation, at different spatial locations are conditionally independent given the numerical weather predictions. The second method models the spatial dependence between forecast errors at different locations. The model parameters are estimated using a Bayesian approach via Markov chain Monte Carlo.We evaluate both methods by comparing their probabilistic forecasts with observations of ice formation for Interstate Highway 90 in Washington State for the 2003-2004 and 2004-2005 winter seasons. The use of the probabilistic forecasts reduces costs by about 50% when compared to deterministic forecasts. The spatial method improves the reliability of the forecasts, but does not result in further cost reduction when compared to the first method.
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