MODELING POSTED SPEED LIMITS FOR LOW-VOLUME ROADS

High speed is a major problem in road safety (Benedetto et al., 2004; Capaldo and Nasti, 2012; Dell’Acqua, 2011a; De Luca et al., 2011). The main benefit of high speed traffic flow is that the cost attributed to travel time is reduced, despite higher running costs together with the associated higher environmental costs. The improvements of vehicle performance and road standards promote even higher speeds. Increased speeding will likely result in augmenting the number of traffic accidents, as well as the severity of the injuries, resulting from high-speed accidents. Many drivers are aware of the negative effects of speed, exceeding the speed limit is nevertheless one of the most frequent traffic violations. A feature of road safety is that inferences based on statistical information from individual studies are often imprecise. Designers need to take expert judgment and results from similar surveys into account when drawing conclusions. Traditional approaches have many points of weakness connected to the inadequacy of the accident database and to the low reliability of statistical methods for the diagnosis of the cause. Techniques based on artificial intelligence have been investigated (De Luca, 2015; Mauro et al., 2013). Hazard analysis approach has given important, very applicable and reliable results (Russo et al., 2014b; Russo et al., 2016a). The best point estimates for the link between speed and accidents suggest that the number of accidents could be substantially reduced; by either enforcing lower speed limits or at least reducing the spread of vehicle speeds. To determine the attitudes of drivers towards speed limits and understand their behavior, a survey (Figure 1) of Italian drivers was undertaken in two roadway environments.