Analytical Assessment of Effective Maintenance Operations on At-Grade Unsignalized… DOI: http://dx.doi.org/10.5772/intechopen.86435

	- Adjusting the current geometric design to the Italian Design Standard without changing the configuration [15, 16].

Scale Risk Model) to identify the most significant roads from the multiscale perspective, which should guarantee better operability of the sites and help allocate

European Directive 2008/96/EC [3] on road safety stressed the central role

functioning of a road network, defining road infrastructures as the third pillar of

and the full Bayes (FB) approach in combination with the previous measures.

Many scholars [4–6] focusing on the road hotspots identified in the light of European Directive objectives suggested calculating crash frequencies and crash rates to rank "black" sites, while others suggested adopting the empirical Bayes (EB) approach

The main reason for using a Bayesian approach is to force the analyst to look at historical data sets or to canvass expert knowledge to determine what is known about the parameters and processes [7–9]. The key difference between Bayesian statistical inference and frequentist statistical methods concerns the nature of the unknown parameters. In the frequentist framework, a parameter of interest is assumed to be unknown, but fixed. In the Bayesian view of subjective probability, all unknown parameters are treated as uncertain and therefore should be described by a probability distribution. Replication is an important and indispensable tool [10], and Bayesian methods fit within this framework because background

Xie et al. [11] worked out a procedure to identify hotspots in a road network, also investigating different contributing factors to road pedestrian safety such as vehicle volumes, road networks, land use, demographic and economic features, and the social media. The researchers identified potential "black" sites by estimating crash costs, considered an accurate safety measure well able to reflect injury

Analysis procedure presented here focuses on intersections: crossing and turning

maneuvers create opportunities for vehicle-vehicle, vehicle-pedestrian, and vehicle-bicycle conflicts that may also result in traffic crashes. Certainly, human error is a contributing factor to road crashes; however, in addition to driver behavior, road engineering and design measures can also make intersections safer.

a. Evaluating a first measure of exposure to crash risk by using a procedure developed by the Italian National Research Council [12], shown in detail in Section 3.1 and in Figure 3, it is useful in ranking black intersections.

b.Computing LoS, determined by ascertaining control delay at each maneuver and estimating crash costs. Delays were assessed by revising specific analytical HCM 2016 [13] models on the basis of field measurements (see Section 3.2 for details). The crash cost estimates were obtained from the mean values of the costs for injuries to people and damage to vehicles made available by the Italian

c. Identifying hotspots: working in accordance with European Directive

2008/96/EC on road safety, the most dangerous intersections where high crash rate, high crash cost, and low-medium LoS were observed (hotspots) were

In particular, the research steps are summarized as follows:

of risk analysis and management as activities that help ensure the good

local resources better during hazardous events.

Transportation Systems Analysis and Assessment

knowledge is integrated into the statistical model.

The work phases are shown in Figure 1.

Ministry of Infrastructures and Transport.

analyzed in greater depth.

164

safety policy.

severity levels.

2. Goals definition

• Modifying the configuration into a roundabout to achieve benefits in terms of a reduction of conflict points, vehicle speed reduction around the central island, and pedestrian safety [5, 17, 18].

The main features of each crash as identified by analyzing the crash reports were as follows: the location where the crashes happened, the number of crashes, injuries, and fatalities, type of crash, type, and number of vehicles involved, road surface conditions, lighting conditions, marking conditions, the number of legs and lanes, lane width, AADTmaj, that is, the AADT on major roads in terms of vehicles per day, and AADTmin, that is, the AADT on minor roads in terms of vehicles per day, the presence of left-turn lanes, median-refuge islands, right-turn lanes, and the diame-

Analytical Assessment of Effective Maintenance Operations on At-Grade Unsignalized…

A total of 827 crashes were recorded in 5 years. The geometric features of each investigated intersection (see Table 1) were established from documents made available by the Regional Administrative Offices. A total of 770 crashes were observed at non-circular intersections, 623 of which were injury crashes, and 147 involved property damage only (PDO) crashes; a total of 1025 injuries were recorded at non-circular intersections, and 12 fatalities occurred. A total of 57 crashes were observed at single-lane roundabouts, of which 36 were injury crashes and 21 PDO crashes; a total of 57 injuries were recorded at single-lane roundabouts,

The crash value for each intersection shows the number of crashes over a 5-year study period, while the frequency of injury crashes refers to the number of injury crashes per year at each intersection during the study period. Figure 2a shows that

Min Mean Max C.V. Min Mean Max C.V.

Features at intersection Non-circular intersections Roundabouts

Total number of crashes 3 4.51 10 0.84 1 1.32 5 0.75 Total number of injury crashes 0 0.99 6 0.79 0 0.86 2 0.44 Number of injuries 0 1.61 7 0.97 0 1.32 4 0.89 Frequency of crashes per year 0.33 0.48 4.33 0.81 0.12 0.18 0.82 0.77 Frequency of injury crashes per year 0 0.22 1.38 0.79 0 0.10 0.13 0.66 Frequency of injuries 0 0.28 2.00 0.93 0 0.21 0.5 0.87 Note: Min—minimum value; Mean—average value; Max—maximum value; C.V.—coefficient of variation, equal to

Hazard maps of injury crash frequency. (a) Crashes vs geometric properties of lanes. (b) Crashes vs traffic and

ter of the roundabouts.

DOI: http://dx.doi.org/10.5772/intechopen.86435

and no fatalities occurred.

the standard deviation divided by the mean value.

Overview of the main statistical features of the crashes and the intersection type.

Table 1.

Figure 2.

167

geometric properties.

f. Assessing the effectiveness of risk management: comparing before and after configurations of the hotspots by calculating the expected LoS and the expected safety effects in terms of crash frequency.

In greater detail, the expected LoS effects were calculated following HCM2016 procedure, but revising, in the light of measurements obtained at study sites; on the other hand, the expected computation of safety effects was performed by adopting the Safety Performance Function (SPF) introduced in [16] according to Highway Safety Manual (HSM) 2010 [17] procedure but revised in the light of study carried out in Southern Italy to which the intersections investigated here belong. Calculation of the expected safety effects obtained from converting the intersections to roundabouts was performed by (a) adopting the analytical models proposed in Rodegerdts et al. [18, 19], whose calibration conditions fit the study context presented here and (b) by using EB analysis to quantify the positive advance of intervention, a common statistical practice in the scientific literature.

This book chapter is organized as follows: Section 2 focuses on data collection, while Section 3 focuses on data analysis for evaluating measures that reflect the exposure of sites to crash risk; the results of the case study are displayed and discussed in Section 4.
