Jamal Abdul-Nasser is a major trunk road that divides Sebha city into two semi-equal parts. It connects almost all districts with different activities and services in the center of the city. Alongside Jamal Abdul-Nasser Street, exist various types of activities: administrative, educational, commercial, public services and some other activities. Due to its importance, the street carries out the densest traffic movement within the city. This paper tries to study and analyze traffic movement at five intersections on Jamal Abdul-Nasser Street: (1) Al-Nahdha; (2) Khaled IbnAl-Walid; (3) Al-Khotoot; (4) Al-Tijari Bank; (5) Al-Zeraie Bank. Selection of the pre-mentioned intersections referred to their importance as converging points to various important activities and land uses. Data required for the study purposes include intersection characteristics, traffic flow characteristics and traffic composition pattern. The results reveal the significance of taking care of the geometric and regulatory aspects that enable the intersections to perform well with regard the smoothness of traffic flow.

Pavement distress is an indication of pavement layer deterioration. There are many types of

deteriorations; however, rutting, defined as the permanent deformation forming longitudinal

surface depressions in the wheel paths, is one of the most important kind of distress that affect

the safety and the ride quality of flexible pavement. The main objective of this study was to

develop an empirical pavement rutting model for the wet freeze zone, which is one of the four

long term pavement performance (LTPP) climate zones, to predict the depth of pavement rutting

on granular bases. Using the LTPP database, the study aimed at a better understanding of the

pavement rutting phenomena and the factors that may affect pavement rutting. Multiple

regression analysis was performed to develop a flexible pavement rutting model. The proposed

model was developed based on the relationship between the response variable rut depth, and

predictor variables of traffic loads, structural number, Marshall stiffness, air voids in the total

mix, and voids in the mineral aggregate. It was found that traffic loads was the predominant

factor that have a significant effect on pavement rutting, which agrees with the existing

literature, as well as engineering knowledge and practice. Following the traffic loads, structural

number was the most significant secondary factor, followed by percent of voids in the total mix,

• voids in the mineral aggregate, and Marshall stiffness.

Various types of pavement deterioration can affect pavement performance, including rutting,

which causes safety and service quality problems on the highways. Rutting, often referred to as

permanent deformation of a pavement surface, causes longitudinal depressions creating

channels in wheel paths. There are many in-service pavement performance databases, but the

Long Term Pavement Performance database (LTTP) is the largest of its kind in the world. It

encompasses data from four different climate zones in North America. Data on flexible

pavements from only the dry freeze zone was included in the scope of the study reported herein.

Regression analysis was performed to develop a rutting model. The proposed model indicates

that the voids in the mineral aggregate (VMA) of hot mixed asphalt is the most important factor

and the positive values of the regression coefficient of VMA implies that rut depth increases

when VMA increases. The other important factors in this model are Marshall stiffness and

freeze index. The negative values of the regression coefficients of Marshall stiffness and freeze

index indicates that the rut depth will decrease when the Marshall stiffness and freeze index

increase.