  Accurate expected time of arrival (ETA) information is crucial in maintaining
the quality of service of public transit. Recent advances in artificial
intelligence (AI) has led to more effective models for ETA estimation that rely
heavily on a large GPS datasets. More importantly, these are mainly cabs based
datasets which may not be fit for bus-based public transport. Consequently, the
latest methods may not be applicable for ETA estimation in cities with the
absence of large training data set. On the other hand, the ETA estimation
problem in many cities needs to be solved in the absence of big datasets that
also contains outliers, anomalies and may be incomplete. This work presents a
simple but robust model for ETA estimation for a bus route that only relies on
the historical data of the particular route. We propose a system that generates
ETA information for a trip and updates it as the trip progresses based on the
real-time information. We train a deep learning based generative model that
learns the probability distribution of ETA data across trips and conditional on
the current trip information updates the ETA information on the go. Our plug
and play model not only captures the non-linearity of the task well but that
any transit agency can use without needing any other external data source. The
experiments run over three routes, data collected in the city of Delhi
illustrates the promise of our approach.
