Increased traffic burdens cities with congestion, pollution, and noise. Smart mobility (SM) applications offer solutions by combining real-time traffic data with simulation-based forecasting to influence commuter behavior and optimize infrastructure use. While prior research has developed traffic simulation methods or assessed SM applications at a high level, detailed implementation guidelines and practical evaluations remain scarce. To address this, we benchmark traffic simulations, showing they scale near-linearly, are cost-effective, and require minimal computational resources. These features enable the creation of extensive scenario libraries for diverse urban contexts. Additionally, we compare traffic simulations with artificial neural network (ANN)-based forecasting, highlighting complementary strengths. Our results underline the potential of combining simulations and ANNs into integrative SM systems, offering potential insights for cities aiming to implement solutions for e-mobility, congestion reduction, and sustainable urban planning.