Accurate and trustworthy short-term traffic prediction is crucial in the modern world for the comfort of drivers and decision-makers as it is used to improve the performance of traffic management systems, lessen congestion, increase safety, and shorten journey times. It is possible to discover useful information for network transportation planning, such as forecasting demand, finding bottlenecks, and prioritizing infrastructure improvements, by concentrating on network-wide traffic prediction. Scholars have developed a variety of methods that can be generally divided into model-based and data-based methods in order to accurately predict network-wide traffic. However, while studies have demonstrated the capability of deep learning methods, particularly convolutional neural networks (CNNs), in predicting traffic states, the complex nonlinear spatial and temporal traffic characteristics, the time-consuming model creation and training, and the unexplained methodology and predictions continue to pose challenges to the task. This thesis seeks to address these issues by analyzing how deep neural networks identify spatiotemporal traffic patterns for network-wide traffic predictions. To this end, a hybrid CNN-RNN model utilizing a pretrained Inception ResNet v2 feature extractor and a long short-term memory encoder-decoder is constructed to forecast network traffic speeds. A pretrained Inception ResNet v2-based image classifier is built based on the predictions to identify traffic patterns, and Grad-CAM is used to explore how the model identifies them. A freeway network in Amsterdam, Netherlands, is used as a case study. While it is expected that the hybrid CNN-RNN model can give comparable performance to the state-of-the-art methods, e.g. the DGCN proposed by Li et al., results indicate that it cannot fully capture the dynamic characteristics of the traffic, nor can it accurately provide predictions. The image classifier failed to identify the distinct traffic patterns as well, despite Grad-CAM's success in indicating locations with rapid changes of values. Overall, the findings highlight the influence of inductive bias on deep learning models, and the importance of fine-tuning and model-data compatibility. Although further research is required, the conclusions are still beneficial to make informed decisions when choosing appropriate models for future network-wide traffic speed prediction tasks.