Work

Generalized Adaptive Smoothing Using Matrix Completion for Traffic State Estimation

Year: 2022

Type: conference-paper

Abstract: The Adaptive Smoothing Method (ASM) is a data-driven approach for traffic state estimation. It interpolates unobserved traffic quantities by smoothing measurements along spatio-temporal directions defined by characteristic traffic wave speeds. The standard ASM consists of a superposition of two a priori estimates weighted by a heuristic weight factor. In this paper, we propose a systematic procedure to calculate the optimal weight factors. We formulate the a priori weights calculation as a constrained matrix completion problem, and efficiently solve it using the Alternating Direction Method of Multipliers (ADMM) algorithm. Our framework allows one to further improve the conventional ASM, which is limited by utilizing only one pair of congested and free flow wave speeds, by considering multiple wave speeds. Our proposed algorithm does not require any field-dependent traffic parameters, thus bypassing frequent field calibrations as required by the conventional ASM. Experiments using NGSIM highway data show that the proposed ADMM-based estimation incurs lower error than the ASM estimation.

Source: IEEE International Conference on Intelligent Transportation Systems (ITSC)

DOI: 10.1109/itsc55140.2022.9921908

Authors: Chuhan Yang, Bilal Thonnam Thodi, 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

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

Funders: New York University Abu Dhabi, Tamkeen