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width, locations of ramps, locations of intersections and etc., different snow plowing patterns, effects of weather conditions and so on. This model contains two difficult parts: the first one is about capturing the accurate traffic speed and plow speed while the other one is about capturing different flow patterns affected by pavement geometry and number of available plows. The ability of snow plows and anti-icing spreader to most efficiently treat roadway surfaces is often impeded by reduced traffic speeds. The primary causes of speed reductions are excessive roadway congestion (Chien et al, 2001, 2002), as one would find during peak travel periods, and event-induced impairment to driving conditions due to poor visibility and treacherous roadway surfaces. Significant research efforts have been devoted to the understanding of the relationship between highway mobility and various factors related to weather, maintenance operations, and traffic. Past research indicates that heavy snow falls could reduce free flow speeds up to 40 percent and highway capacities up to 30 percent as compared to those during normal weather conditions (Ibrahim and Hall, 1994; HCM 2000). In addition, it has been found that travel times in adverse weather conditions such as fog, ice, snow storms would increase up to 36 percent on major US highways (Han et al., 2003). According to FHWA (2008), adverse weather accounts for approximately 544 million vehicle-hours of delay per year or 23 percent of the total non-recurrent delay on the US highways. Manish et al. (2005) quantified the impact of rain, snow, and pavement surface conditions on freeway traffic flow for the metro freeway region around the Twin Cities. The research database included four years of traffic data from in-pavement system detectors, weather data over the same period from 3 automated surface observing systems (ASOS), and two years of available weather data from 5 road weather information systems (RWIS). Their research classified weather events by their intensities and identified how changes in weather type and intensities impacted the speed, headways, and capacity of roadways. Datasets on snowfall events were categorized into five categories of none, trace, light, moderate, and heavy (0, less than/equal to 0.05, 0.06–0.1, 0.11–0.5, and greater than 0.5 inches/hour, respectively). Speed reductions of 3%–5%, 7%–9%, and 8%–10% for trace, light, and moderate snow, respectively, were obtained, which quantifies reduction in speeds better than recommended speed reductions of 8%–10% in the Highway Capacity Manual 2000 due to light snow only. In contrast, speed reductions of 11%–15% for heavy snow (more than 0.5 inches/hour) significantly differ from the recommended speed reductions (25%–35%) in the Highway Capacity Manual 2000. Daniel and Chien (2009) investigated the impact of adverse weather on New Jersey roadways by collecting traffic parameters under a variety of weather and light conditions. Speed, flow and density data were collected under no adverse weather, as well as under rain, snow, darkness and sun glare. The study found that under snow conditions speeds decreased between 5.8 mi/hr (9.3 km/hr or 15%) to 33.8 mi/hr (54 km/hr or 50%). Chien (2009) also found that snow covered roadways had the greatest impact on traffic when compared with rain or snow events. TRB 2012 Annual Meeting Paper revised from original submittal.
