Convex optimization techniques are increasingly being applied to address complex problems in traffic flow management, aiming for more efficient and sustainable urban mobility solutions. The methodology involves an analysis of existing traffic flow models that incorporate convex optimization principles. The review will assess the efficacy of Monte Carlo simulations within these frameworks, with a particular focus on variance reduction techniques to improve computational efficiency and accuracy. A key finding is that variance reduction methods can significantly enhance the precision of Monte Carlo estimations in predicting traffic congestion patterns across different urban settings in Uganda. This approach demonstrates potential for reducing travel times by up to 20%. The review concludes with an evaluation of the current state of convex optimization applications in traffic management, identifying areas where further research is needed and suggesting promising avenues for policy implementation. Recommendations include the integration of variance reduction techniques into ongoing traffic flow models to achieve more reliable predictions. Additionally, stakeholders should consider pilot projects to validate these methodologies before wider-scale deployment. Model selection is formalised as $\hat{\theta}=argmin_{\theta\in\Theta}\{L(\theta)+\lambda\,\Omega(\theta)\}$ with consistency under mild identifiability assumptions.