Urban planning in Cairo, Egypt faces significant challenges due to rapid urbanization, population growth, and limited resources. The study employed a mixed-methods approach, combining quantitative analysis of traffic flow data with qualitative interviews to understand user experiences and needs. Analysis revealed that the average daily traffic congestion is reduced by 15% when optimal routes are recommended using big data analytics compared to current methods. User satisfaction scores increased by 20%, indicating a positive impact on service delivery. Big data analytics can significantly enhance urban planning and service efficiency in Cairo, particularly in managing traffic flow and improving public transportation services. Implementing real-time traffic management systems and expanding the use of big data for urban planning could further improve city operations. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.