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Experimental assessment of traffic density estimation at link and network level with sparse data
- Source :
- Transportmetrica B: Transport Dynamics, Transportmetrica B: Transport Dynamics, Taylor & Francis, In press, ⟨10.1080/21680566.2021.2002738⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- 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.
- Subjects :
- [SPI.OTHER]Engineering Sciences [physics]/Other
0209 industrial biotechnology
Computer science
FUSION DE DONNEES
highway
Transportation
02 engineering and technology
LOOP DETECTORS
computer.software_genre
PROBE VEHICLES
FUSING TRAFFIC DATA
020901 industrial engineering & automation
0502 economics and business
Network level
TRAFIC ROUTIER
waves
state estimation
DENSITE DU TRAFIC
Sparse matrix
050210 logistics & transportation
TRAFFIC STATE ESTIMATION
05 social sciences
Link (geometry)
Density estimation
calibration
MODELISATION
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
GESTION DU TRAFIC
TRAITEMENT EN TEMPS REEL
Modeling and Simulation
flow
vehicle
SIMULATION
Data mining
computer
Software
DENSITY ESTIMATION
Subjects
Details
- Language :
- English
- ISSN :
- 21680566 and 21680582
- Database :
- OpenAIRE
- Journal :
- Transportmetrica B: Transport Dynamics, Transportmetrica B: Transport Dynamics, Taylor & Francis, In press, ⟨10.1080/21680566.2021.2002738⟩
- Accession number :
- edsair.doi.dedup.....cbbdc615ae7931c8a89655c22866ee7f