Traffic accidents in Jordan represent a serious problem, where every day about 2 persons die by road accidents, and the country loses between 2-3 percent of its gross domestic products (GDP) due to this problem. This research aims to estimate the total and unit costs (per crash severity and vehicle type) of road accidents in Jordan during three years (2011 through 2013) using human capital approach, and also to estimate the value of statistical life in Jordan for risk reduction of person’s death by road accident using willingness-to-pay approach. The results revealed that Jordan is still suffering from a continuous rise in its road accidents’ costs without a sufficient improvement accompanied with the dramatic increases in its number of vehicles and population (during the study period), especially after the crisis of Syrian refugees who enter the Jordan during this period. The estimated total costs of accident in the years 2011, 2012 and 2013 were about US $ (3814, 4718 and 5146, respectively, which constituted 2.5%, 2.3% and 2.25% of the total country’s GDP for the same years.

The objective of the paper is to define a complex methodology to analyze black spot locations of road infrastructure network combining the benefit of both; Empirical Bayes method and K-mean clustering approach. In the first step, K-mean algorithm is used to define homogeneous accident clusters. The homogeneity is described in three terms: traffic conditions, geometric design of the road and accident characteristics. Then, Empirical Bayes method is applied to define black spots based on the determined clusters. Due to the combination of the introduced methods, a powerful technique is provided that is able to identify high-risk locations and cluster dependent segment length as the output of the model.

In road safety, the process of organizing road infrastructurenetwork data into homogenous entities is called segmentation.Segmenting a road network is considered thefirst and most important step in developing a safety performancefunction (SPF). This article aims to study the benefitof a newly developed network segmentation method which is based on the generation of accident groups applying K-means clustering approach. K-means algorithm has been used to identify the structure of homogeneous accident groups. According to the main assumption of the proposed clustering method, the risk of accidents is strongly influenced by the spatial interdependence and traffic attributes of the accidents. The performance of K-means clustering was compared with four other segmentation methods applying constant average annual daily traffic segments, constant length segments, related curvature characteristics and a multivariable method suggested by the Highway Safety Manual (HSM). The SPF was used to evaluate the performance of the five segmentation methods in predicting accident frequency. K-means clustering-based segmentation method has been proved to be more flexible and accurate than the other models in identifying homogeneous infrastructure segments with similar safety characteristics.

This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.