An effective transportation system is important for supporting various human activities in a modern smart city. The waiting time at various stations has great impacts on the overall transportation system efficiency and people's health like stress and anxiety. Knowing the waiting time at different locations in advance can assist the travelers to plan their trips. However, such waiting time may depend on many factors like crowdedness and the collective travel behaviors of the travellers involved. In general, it is very expensive to collect all the required data at every location. In this paper, a deep learning approach is proposed for determining the waiting time levels at public transport stations based on some proxy data and limited historical waiting time data at some stations. We formulate the public transportation network as a graph and develop a semi-supervised classification model based on Graph Convolutional Networks which can operate directly on the graph-structured data with limited labelled data. We conduct experiments for the mass transit railway in Hong Kong with real data and our proposed approach can achieve 89% accuracy of classifying the waiting time levels.