Traffic forecasting is key to improving urban transport and reducing congestion and pollution. While advanced models like Graph Neural Networks (GNNs), can capture complex patterns in traffic flow, they are resource-intensive and do not scale well. This problem can be mitigated by using simpler models that are less influenced by the size of the road network, making them more practical for real-world applications. This study investigates whether simpler network-based models, particularly Long Short-Term Memory (LSTM) networks, can match or surpass the performance of GNNs, such as the Diffusion Convolutional Recurrent neural network (DCRNN), in specific scenarios. Using popular benchmark datasets, we compared the performance of the LSTM and DCRNN models under different conditions, including different sensor distributions and prediction horizons. The results indicate that while DCRNN highly outperforms LSTM with numerous sensors and longer prediction horizons, LSTM gives promising results with fewer sensors and shorter horizons. In this scenario, the difference in performance is minimal regardless of the location of sensors, also offering significant computational efficiency. These findings suggest that LSTM models may be a practical alternative for traffic forecasting in resource-constrained scenarios, providing a path to more efficient urban traffic management.