  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.
