This paper presents a Bayesian data fusion framework for estimating journey time variability that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework offers a generalized statistical foundation for making full use of multiple traffic data sources to estimate the vehicular journey time variability. Feeding data collected from multiple data sources are classified based on the associated traffic conditions, and the corresponding estimation biases of the individual data sources are determined by arbitrary distributions. The proposed framework is implemented and tested on a Hong Kong corridor with actual data collected from the field. Different statistical distributions of prior and likelihood knowledge are applied and compared. The findings of the case study show significant improvement in the journey time estimations of the proposed method compared with the individual measurements. The results also highlight the benefit of incorporating a traffic state classifier and prior knowledge in the fusion framework. This study contributes to the development of reliability-based intelligent transportation systems based on advanced traffic data analytics.