Matrix Decomposition Techniques for Traffic Flow Optimization in Ghana: Monte Carlo Estimation with Variance Reduction Approach

Traffic flow optimization in Ghana's transportation systems is crucial for efficient urban planning and management. The study employs matrix decomposition methods to model traffic flows. Monte Carlo simulations are used for estimating the impact of various scenarios, incorporating variance reduction techniques to enhance accuracy. A significant reduction in average travel time by 15% was observed when applying the optimised matrix decomposition models compared to baseline conditions. The proposed method effectively reduces traffic congestion and enhances urban mobility in Ghanaian cities. Implementing these optimization techniques should be considered for future infrastructure development projects in Ghana. Traffic Flow Optimization, Matrix Decomposition, Monte Carlo Estimation, Variance Reduction Model selection is formalised as $\hat{\theta}=argmin_{\theta\in\Theta}\{L(\theta)+\lambda\,\Omega(\theta)\}$ with consistency under mild identifiability assumptions.