Pavement infrastructure is essential and must be protected with limited resources. For decades, industrialized nations have used Pavement Management Systems (PMS) and pavement distress assessment to examine network and project-level pavement conditions. Pavement condition models can anticipate pavement degradation, schedule maintenance, and create multi-year rehabilitation plans based on historical data. Pavement condition surveys are done annually or biannually to calibrate pavement condition models and reduce network maintenance costs. This study highlights road system deficiencies to meet traffic demand, improper systematic methods of maintaining networks, budget constraints for decision makers, deficiencies in road geometrics, poor construction and maintenance practices, the need for proper planning and resource management, and improper planning. The technique comprises a theoretical element of PMS and a review of performance evaluation studies. A PMS lets users export data in an easy-to-understand manner, helping them manage their roadways better. Manual pavement distress surveys need certified raters.

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Speed, flow, and density are the most effectiveness traffic parameters. For the study area, all required speed-flow data were collected manually by special team using the necessary survey equipment from 11:00am-6:00pm during one week within different days for each direction of Al-Doura Expressway in Baghdad city. Greenshield Model (GSM) and Greenberg Model (GBM) have been analyzed using EXCEL software to compare the implementation results of the real data. The calibration of regression analysis studies were used and the statistical coefficient of person's correlation (R) and coefficient of determination (R2) were computed. It was found the following: For the direction of Baghdad greater bridge to Al-Rasheed camp, models according to GSM and GBM are us=110.84–0.37k and us=59.24ln(320.72/k) respectively. For the second direction, the models are us=64.04–0.10k and us=11.99ln(2937.5/k) respectively. All models are achieved strong correlation between variables ( between 0.88-0.98, and high R2 between 0.77-0.97). The models according to Greenberg ascertain better fit due to values between 0.95-0.98 and due to R2 values between 0.90-0.97 which are closest to 1.00 and these represent the coefficients for the first and second directions respectively. Finally, u0 and k0 at maximum flow have been considered based on GBM showing that the traffic capacity equal to 6990.08 and 12959.17 vph for the two sides. This mean that Al-Doura Expressway serve the study area with a 35-65 of directional split. This distribution of traffic between the two directions is nearly coincide with the distribution of the real data 34.6-65.4.