Probabilistic Data Fusion for Short-Term Traffic Prediction with Semiparametric Density Ratio Model

Data fusion is an approach that combines multiple data sources for a more efficient statistical purpose. There have been some explorations on the application of data fusion for short-term traffic predictions. Unlike the previous work, this paper attempts to propose a probabilistic data fusion approach. This approach regards different data sources as random variables with some empirical distributions, and it attempts to fuse the data sources with the consideration of their probability distributions so as to improve probabilistic inference and hypothesis test. The density ratio model is introduced and utilized for this probabilistic data fusion approach, which estimates a fused probability distribution with different data sources. Real-world case studies are conducted to investigate the goodness-of-fit of the probabilistic data fusion and its impact on traffic predictions. This paper finds that probabilistic data fusion can improve the prediction accuracy when the fused probability distribution contains 'incomplete' characteristics of the empirical distribution.