Generalized adaptive smoothing based neural network architecture for traffic state estimation

TL;DR

This paper introduces neural network architectures, ASNN and MASNN, that automatically tune parameters of the adaptive smoothing method for traffic state estimation, outperforming traditional heuristic approaches on real data.

Contribution

The paper presents a novel neural network framework that learns to optimize adaptive smoothing parameters, improving traffic state estimation accuracy.

Findings

ASNN and MASNN outperform conventional ASM in experiments

MASNN leverages ensemble averaging for stronger predictions

Neural networks effectively tune ASM parameters from sparse sensor data

Abstract

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.