The objective of this study was to identify the most salient driver faults that cause crashes on some Jordanian rural and suburban roadway segments, to examine crashes with distracted driving as the driver’s fault, and to investigate the differences between crashes caused by distracted driving. Data for more than 10,200 crashes on nine roadway segments (five rural and four suburban) were accessed from the relevant government agency, but only n = 2472 were used for analysis after controlling for crashes specified as being caused by drivers’ distracted driving. IBM SPSS version 22 was used to perform descriptive analysis and independent samples’ t-tests. The results revealed that distracted driving was the second most common driver fault to cause crashes and the second main cause of fatalities and injuries on both rural and suburban roadways. Distracted driving on rural highways appears to be more fatal, whereas it caused more crashes with severe injuries on suburban roads. The variables at junction, road grade, number of lanes, weather condition, crash type, and number of vehicles involved were found to be statistically significant but with a small effect size. The following categories showed high percentages of distracted driving crashes on rural and suburban roadways: males, drivers 25–39 years old, non-holidays, weekdays, tangent sections, two-way divided roads, not at junction, level roads, two-lane roads, clear weather, dry surface, daylight, and automobile vehicles showed high percentages of distracted driving crashes on rural and suburban roadways. Differences between crashes on rural and suburban roadways caused by distracted driving were found to be small.

This paper presents the development of a methodology for the classification of aggregates based on their shape, angularity, and texture characteristics. This methodology utilizes the Aggregate Imaging System (AIMS) to measure aggregate characteristics, and the clustering statistical method to analyze the measurements. The outcome of this analysis method is the percentage of aggregate particles that belong to groups or clusters that have statistically different characteristics. The “Categorical Counts” method is employed in this study for comparing the characteristics of different aggregate samples. This method detects not only the statistical difference between aggregate samples, but it is also capable of identifying differences in each of the clusters. The quality of AIMS measurements is evaluated through the analysis of repeatability and reproducibility. Finally, statistical analysis was conducted to determine whether aggregate properties are significant enough to influence the measured mechanical properties of a wide range hot mix asphalt (HMA) mixtures or not. Although the results showed that aggregate shape characteristics had strong correlations with measured mechanical properties, the data was not comprehensive enough to develop predictive equations of mechanical properties as functions of aggregate and other mixture properties. Discussion is provided on the laboratory experimental factors that influence the correlations between aggregate characteristics and measured mixture mechanical properties.

Due to limited rainfall and precipitations, different developing countries depend on groundwater (G.W.) resources to challenge water scarcity. This practice of continuous and excessive G.W. pumping has led to severe water shortages and deteriorated water quality in different countries. Recharging of treated wastewater (TWW) into G.W. provides a critical solution for solving water scarcity, extending the well’s service life, and maintaining the G.W. supply. However, effective injection practice requires accurate tools and methods to determine the best location for groundwater recharge (GWRC). This work offers a new tool based on GIS–Multi-Criteria Analysis to identify the potential site and locations for GWRC with TWW. The developed methodology was applied to one of the most used well-field areas in Jordan (Dhuleil-Halabat). The G.W. flow for the B-B2/A7 formation system in the area of study was simulated using Processing Modflow (version 8.0). The analysis combined six thematic maps produced following the environmental, technical, and economic criteria to draw conclusions and recommendations. Both steady and transient conditions were used to predict the future changes that might occur under different stresses and after continuous GWR. The study evaluated three possible scenarios of artificial GWRC to evaluate the process efficiency and determine the effect on the water table level. The results revealed that only 0.05% (0.14 Km²) of the total surface area of 450 Km² is suitable for GWRC. A GWRC with TWW at a rate of 3.65 Mm³/year (MCMY) would provide a good G.W. table recovery to 39.68 m in the year 2025, maintain a steady-state water table ≥ of 50.77 m for up to six years, and secure water supply for future generations. The proposed methodology can be used as a useful tool that can be applied to regulate the GWRC practice worldwide.

Pedestrian safety is a major concern throughout the world because pedestrians are considered to be the most vulnerable roadway users. This paper sought to identify the main factors in pedestrian-vehicle crashes that increase the risk of a fatality or severe injury. Pedestrian-vehicle crashes which occurred in urban and suburban areas in Jordan between 2009 and 2011 were investigated. Extracted rules from Bayesian networks were used to identify factors related to severity of pedestrian-vehicle crashes. To obtain as much information as possible about these factors, three subsets were used. The first and second subsets contain all types of collisions (pedestrian and nonpedestrian), in which the first subset used collision type as a class variable and the second subset used injury severity. The third subset contains pedestrian collisions only and used injury severity as the class variable. The results indicate that when using collision type as the class variable, better performance was obtained and that the following variables increase the risk of fatality or severe injury: roadway type, number of lanes, speed limit, lighting, and adverse weather conditions.