Traffic State Estimation on Urban Roads Using Perception-Enriched Floating Car Data

Moritz Schweppenhäuser, Karl Schrab, Robert Protzmann, Ilja Radusch

2024

Abstract

Modern-day navigation systems by developers like Google© and TomTom© require user participation primarily in the form of Floating Car Data (FCD) for accurate Traffic State Estimation (TSE). However, to provide reliable information, systems rely on large road user participation of at least 5 %, which is only truly available to the big players. We propose a method to soften the participation requirement by utilizing modern perception sensors (e.g., radar, lidar, camera) of connected vehicles (CVs) to enrich the FCD set, compensating reduced data quantity with increased data quality. By using position and speed estimates of surrounding vehicles we increase the sample size and can additionally collect estimates of segments that are not traversed by CVs. To validate and assess the proposed system, we utilize Eclipse MOSAIC and conduct a simulation-based test series on the calibrated large-scale BeST scenario. Initial findings indicate improved estimation performance on selected road segments, especially at lower rates of market penetrations. In a network-wide investigation, we show that travel time estimates of the proposed method are often more accurate than conventional approaches, while also requiring smaller penetration rates.

Paper Citation

in Harvard Style

Schweppenhäuser M., Schrab K., Protzmann R. and Radusch I. (2024). Traffic State Estimation on Urban Roads Using Perception-Enriched Floating Car Data. In Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS; ISBN 978-989-758-703-0, SciTePress, pages 99-111. DOI: 10.5220/0012620500003702

in Bibtex Style

@conference{vehits24,
author={Moritz Schweppenhäuser and Karl Schrab and Robert Protzmann and Ilja Radusch},
title={Traffic State Estimation on Urban Roads Using Perception-Enriched Floating Car Data},
booktitle={Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS},
year={2024},
pages={99-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012620500003702},
isbn={978-989-758-703-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS
TI - Traffic State Estimation on Urban Roads Using Perception-Enriched Floating Car Data
SN - 978-989-758-703-0
AU - Schweppenhäuser M.
AU - Schrab K.
AU - Protzmann R.
AU - Radusch I.
PY - 2024
SP - 99
EP - 111
DO - 10.5220/0012620500003702
PB - SciTePress