Urban traffic congestion remains a pressing challenge in Erbil, particularly at signalized intersections where delays contribute to fuel consumption, emissions, and commuter frustration. This study presents a calibrated microsimulation model using PTV VISSIM to replicate field-measured control delays at a key intersection in Erbil. Field data were collected through video-based observations and analyzed to establish baseline performance. The simulation was calibrated using manual adjustments to driver behavior and signal timing parameters, constrained by the student version of the software. The model's accuracy was evaluated through statistical comparison with field data. Results showed a strong correlation (R=0.938) and a high coefficient of determination (R2=0.879), indicating that nearly 88% of the variation in simulated delay could be explained by observed conditions. Error metrics further supported the model's reliability, with a root mean square error (RMSE) of 7.31 seconds per vehicle, a mean absolute error (MAE) of 5.92 seconds, and GEH statistics consistently below 2, well within accepted thresholds for traffic modeling. While the study was limited to a single intersection due to software constraints, the findings offer practical insights for traffic engineers and policymakers. Recommendations include adopting adaptive signal control systems and integrating intelligent transportation technologies to improve intersection performance. Future research should expand the model to multiple intersections, incorporate real-time data, and explore environmental impacts. This study provides a localized, data-driven foundation for improving urban mobility in Erbil through simulation-based planning.
Several modal split models have been created around the world to forecast which mode of transportation will be selected by the trip - maker from among a variety of available modes of transportation. This modeling is essential from a planning standpoint, as transportation systems typically receive significant investment. In this study, the main purpose was to develop a mode choice model using multiple linear regressions for Ramadi city in Iraq. The study area was divided into traffic analysis zones (TAZ) to facilitate data collection. The data was collected through a home interview of the trip makers in their home units through a questionnaire designed for this purpose. The result showed that the most influential factors on the mode choice for the general trips model using multiple linear regressions are car ownership, age, and trip cost. This model gave a good correlation coefficient of 0.829 meaning that the independent variables explain 82.9 of variance in the dependent variable (type of mode), which will help transport planners in developing policies and solutions for future
RSM and DOEs approach were used to optimize parameters for hypoeutectic A356 Alloy. Statistical analysis of variance (ANOVA) was adopted to identify the effects of process parameters on the performance characteristics in the inclined plate casting process of semisolid A356 alloy which are developed using the Response surface methodology (RSM) to explain the influences of two processing parameters (tilting angle and cooling length) on the performance characteristics of the Mean Particle Size (MPS) of α-Al solid phase and to obtain optimal level of the process parameters. The residuals for the particle size were found to be of significant effect on the response and the predicted regression model has extracted all available information from the experimental data. By applying regression analysis, a mathematical predictive model of the particle size was developed as a function of the inclined plate casting process parameters. In this study, the DOEs results indicated that the optimum setting was approx. (44) degree tilt angle and (42) cm cooling length with particle size (30.5) μm