Traffic State Estimation in Congestion to Extend Applicability of DFOS

Fuente: arXiv
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Autores principales: Yajima, Yoshiyuki, Prasad, Hemant, Ikefuji, Daisuke, Sakurai, Hitoshi, Otani, Manabu
Formato: Preprint
Publicado: 2025
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author Yajima, Yoshiyuki
Prasad, Hemant
Ikefuji, Daisuke
Sakurai, Hitoshi
Otani, Manabu
author_facet Yajima, Yoshiyuki
Prasad, Hemant
Ikefuji, Daisuke
Sakurai, Hitoshi
Otani, Manabu
contents This paper presents a traffic state estimation (TSE) method in congestion for distributed fiber-optic sensing (DFOS). DFOS detects vehicle driving vibrations along the optical fiber and obtains their trajectories in the spatiotemporal plane. From these trajectories, DFOS provides mean velocities for real-time spatially continuous traffic monitoring without dead zones. However, when vehicle vibration intensities are insufficiently low due to slow speed, trajectories cannot be obtained, leading to missing values in mean velocity data. It restricts DFOS applicability in severe congestion. Therefore, this paper proposes a missing value imputation method based on data assimilation. Our proposed method is validated on two expressways in Japan with the reference data. The results show that the mean absolute error (MAE) of the imputed mean velocities to the reference increases only by 1.5 km/h as compared with the MAE of non-missing values. This study enhances the wide-range applicability of DFOS in practical cases.
format Preprint
id arxiv_https___arxiv_org_abs_2508_21138
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Traffic State Estimation in Congestion to Extend Applicability of DFOS
Yajima, Yoshiyuki
Prasad, Hemant
Ikefuji, Daisuke
Sakurai, Hitoshi
Otani, Manabu
Systems and Control
Statistical Mechanics
Cellular Automata and Lattice Gases
Physics and Society
This paper presents a traffic state estimation (TSE) method in congestion for distributed fiber-optic sensing (DFOS). DFOS detects vehicle driving vibrations along the optical fiber and obtains their trajectories in the spatiotemporal plane. From these trajectories, DFOS provides mean velocities for real-time spatially continuous traffic monitoring without dead zones. However, when vehicle vibration intensities are insufficiently low due to slow speed, trajectories cannot be obtained, leading to missing values in mean velocity data. It restricts DFOS applicability in severe congestion. Therefore, this paper proposes a missing value imputation method based on data assimilation. Our proposed method is validated on two expressways in Japan with the reference data. The results show that the mean absolute error (MAE) of the imputed mean velocities to the reference increases only by 1.5 km/h as compared with the MAE of non-missing values. This study enhances the wide-range applicability of DFOS in practical cases.
title Traffic State Estimation in Congestion to Extend Applicability of DFOS
topic Systems and Control
Statistical Mechanics
Cellular Automata and Lattice Gases
Physics and Society
url https://arxiv.org/abs/2508.21138