Work

Generalized adaptive smoothing based neural network architecture for traffic state estimation

Year: 2023

Type: article

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 with two real-world datasets. Our experiments reveal that the ASNN and the MASNN outperform the conventional ASM.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2023.10.1502

Authors: Chuhan Yang, Ambadipudi Sai Venkata Ramana, Saif Eddin Jabari

Institutions: New York University Abu Dhabi, New York University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: New York University Abu Dhabi, Tamkeen