This research paper provides a framework for the efficient representation and analysis of both spatial and temporal dimensions of panel data. This is achieved by representing the data as spatio-temporal image-matrix, and applied to a case study on forecasting public transport ridership. The relative performance of a subset of machine learning techniques is examined, focusing on Convo-lutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural networks. Furthermore Sequential CNN-LSTM, Parallel CNN-LSTM, Augmented Sequential CNN-LSTM are explored. All models are benchmarked against a Fixed Effects Ordinary Least Squares regression. Historical ridership data has been provided in the framework of a project focusing on the impact that the opening of a new metro line had on ridership. Results show that the forecasts produced by the Sequential CNN-LSTM model performed best and suggest that the proposed framework could be utilised in applications requiring accurate modelling of demand for public transport. The described augmentation process of Sequential CNN-LSTM could be used to introduce exogenous variables into the model, potentially making the model more explainable and robust in real-life settings.