2023
DOI: 10.1016/j.ijtst.2022.05.007
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Estimating operating speed for county road segments – Evidence from Italy

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Cited by 16 publications
(11 citation statements)
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“…Conversely, speed parameter showed a monomodal shape with a single peak around 66 km/h. This is an expected result, as it is well known that vehicular speed tends to be normally distributed (e.g., Martinelli et al, 2022a;Martinelli et al, 2022b). Similarly, width parameter presented a monomodal form and was densely distributed around 2.4 m value, indicating a likely low variability of this vehicular characteristic among the different subsets.…”
Section: Resultssupporting
confidence: 66%
“…Conversely, speed parameter showed a monomodal shape with a single peak around 66 km/h. This is an expected result, as it is well known that vehicular speed tends to be normally distributed (e.g., Martinelli et al, 2022a;Martinelli et al, 2022b). Similarly, width parameter presented a monomodal form and was densely distributed around 2.4 m value, indicating a likely low variability of this vehicular characteristic among the different subsets.…”
Section: Resultssupporting
confidence: 66%
“…Thus, free- moving vehicles in sub-sample 3 in Figure 6 can be filtered from the overall data using the headway threshold and further analyzed to determine speed percentiles in free-flow traffic conditions. For example, as introduced in the Introduction section, this can be useful in the analysis of operational speed (i.e., the 85th percentile of operating speed distributions or V 85 ), that is a critical factor for road safety, since it significantly influences the frequency and severity of accidents [ 72 , 73 ], and it is widely acknowledged as a benchmark value for evaluating consistency in homogeneous road sections [ 74 , 75 , 76 ]. Figure 7 illustrates the experimental and best-fitting distributions (Gumbel Max for CU 218 and Chi-Squared for CU 1333) and the corresponding V 85 values in the two monitored sections from the speed data in sub-sample 3.…”
Section: Results: Free-moving and Constrained Vehicles Analysismentioning
confidence: 99%
“…The framework includes four data sources and follows three stages and seven steps. It integrates the methodologies previously introduced in Bonera et al [6,22,23] regarding RNS (Steps 1 and 2), and Martinelli et al [37,38] regarding the computation of operating speed (Steps 4 and 5). These are further integrated with a novel approach to RISMS (Steps 6 and 7) and road crash georeferencing (Step 3) to map criticalities and propose potential countermeasures.…”
Section: Methodsmentioning
confidence: 99%