1. Literature review

The key role played by transportation networks in social well-being and safeguarding the world economy means priorities must be established to maintain an adequate level of service and functionality and adequately managing existing weak areas as well as possible hazardous events: a thorough examination of activities within the system, potential risks for users, and the careful management of the planning phase of controls and maintenance operations can help reduce, if not even prevent, failures in the system that may compromise good operation and endanger health, safety, and the environment.

Dickey and Santos [1] identified the response time of emergency services during hazardous events in the transportation system—one of the fundamental actions in restoring disrupted infrastructures—and in guaranteeing essential levels of service and safety to users.

Freiria et al. [2] considered the road transport system as one of the most critical infrastructures in hazard situations performing an LRSRM model (Local Regional

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

European Directive 2008/96/EC [3] on road safety stressed the central role of risk analysis and management as activities that help ensure the good functioning of a road network, defining road infrastructures as the third pillar of safety policy.

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

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 knowledge is integrated into the statistical model.

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 severity levels.
