Generalized adaptive smoothing based neural network architecture for traffic state estimation
This work addresses traffic state estimation for transportation systems, but it is incremental as it builds on an existing method with neural network enhancements.
The authors tackled the problem of traffic state estimation by proposing neural networks that automatically tune parameters of the adaptive smoothing method, which are typically set heuristically and lead to unphysical predictions. Their models, ASNN and MASNN, outperformed the conventional ASM on real-world datasets.
The adaptive smoothing method (ASM) is a standard data-driven technique used in traffic state estimation. The ASM has free parameters which, in practice, are chosen to be some generally acceptable values based on intuition. However, we note that the heuristically chosen values often result in un-physical predictions by the ASM. In this work, we propose a neural network based on the ASM which tunes those parameters automatically by learning from sparse data from road sensors. We refer to it as the adaptive smoothing neural network (ASNN). We also propose a modified ASNN (MASNN), which makes it a strong learner by using ensemble averaging. The ASNN and MASNN are trained and tested two real-world datasets. Our experiments reveal that the ASNN and the MASNN outperform the conventional ASM.