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research article

Experimental assessment of traffic density estimation at link and network level with sparse data

Takayasu, Anna
•
Leclercq, Ludovic
•
Geroliminis, Nikolas  
2022
Transportmetrica B-Transport Dynamics

This paper investigates the accuracy of mean density estimation from direct sensing at link and network levels. Different calculation methods are compared depending on sensor type, probe vehicles or loop detectors, and availability to quantify the magnitude of expected errors. Probe data are essential to reduce the error but accurate density estimation requires high penetration rates, which is hardly true in practice. We enhance the fishing rate method, i.e. using the ratio of probes detected at the loop locations over the loop flow, to estimate density. Accurate density estimation at the link level can only be obtained when probes and loop data are available in real-time. At the network level, accurate density estimations can be obtained when combining loop and probe observations, even if few links capture both data sources. It requires applying the proper analytical formulation to aggregate the local observations, i.e. carefully defining fishing rates at this scale.

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Type
research article
DOI
10.1080/21680566.2021.2002738
Web of Science ID

WOS:000719234800001

Author(s)
Takayasu, Anna
Leclercq, Ludovic
Geroliminis, Nikolas  
Date Issued

2022

Publisher

TAYLOR & FRANCIS LTD

Published in
Transportmetrica B-Transport Dynamics
Volume

10

Issue

1

Start page

368

End page

395

Subjects

Transportation

•

Transportation Science & Technology

•

density estimation

•

loop detectors

•

probe vehicles

•

fusing traffic data

•

traffic state estimation

•

state estimation

•

flow

•

calibration

•

highway

•

vehicle

•

waves

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LUTS  
Available on Infoscience
December 4, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/183550
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