The taxi and passenger queue contexts indicate the various states of queues related to taxis and passengers (i.e. taxis are waiting for passengers, passengers are waiting for taxis, both are waiting for each other, none is waiting). Predicting these queue contexts in a future time is very important for better airport ground transport operations. However, queue context prediction at the airport is a challenging problem due to the presence of different contextual factors i.e., time, weather, taxi trips, flight arrivals and many more. Also these taxi and passenger queue contexts at the airport are imbalanced since some of the contexts are very infrequently occurring compared to others. In this paper, we address the problem of predicting imbalanced taxi and passenger queue contexts at the airport. First, we investigate different contextual factors, including time, taxi trips, passengers and weather for queue context prediction. Then we propose a detailed step by step solution to address this problem. To support the effectiveness of our detailed approach, we generate a queue context dataset by fusing three real world datasets including taxi trip, passenger wait time and weather condition that represent the taxi and passenger queue contexts at a major international airport in the New York City. The experimental results demonstrate that our developed queue context prediction framework provides detailed solutions to deliver higher accuracy in queue context prediction.