Estimation of road link travel time serves a critical role in intelligent transportation operation and management. Due to the uncertainty nature contributed by the volatile traffic, travel time estimates are better described by probability distributions than deterministic models. Existing travel time distribution estimation approaches are mostly based on predefined probability distributions. Other approaches, while relaxing the constraint, fail to utilize the topological information and are data-inefficient. In this paper, we propose a novel Bayesian and geometric deep learning-based approach to estimate the travel time distributions of road links within citywide transportation networks based on vehicular GPS trajectories. Particularly, historical or real-time trajectories are first pre-processed to construct partial travel time maps, which are input into a tailor-made Bayesian graph autoencoder to reconstruct multiple complete travel time maps. We further adopt an auxiliary neural network to facilitate the parameter training of the proposed approach following adversarial training principles. To evaluate the proposed approach, we employ a real-world vehicular trajectory dataset in a series of comprehensive case studies. The empirical results indicate that the proposed approach outperforms the best-performing state-of-the-art baseline with an approximately 10% Kullback-Leibler divergence reduction.