Data fusion algorithm for macroscopic fundamental diagram estimation. (October 2016)
- Record Type:
- Journal Article
- Title:
- Data fusion algorithm for macroscopic fundamental diagram estimation. (October 2016)
- Main Title:
- Data fusion algorithm for macroscopic fundamental diagram estimation
- Authors:
- Ambühl, Lukas
Menendez, Monica - Abstract:
- Highlights: Improvement of MFD through data fusion of loop detector data and floating car data. Estimation of probe penetration rate through loop detectors. Case studies using simulation of abstract grid network and city of Zurich network. Abstract: A promising framework that describes traffic conditions in urban networks is the macroscopic fundamental diagram (MFD), relating average flow and average density in a relatively homogeneous urban network. It has been shown that the MFD can be used, for example, for traffic access control. However, an implementation requires an accurate estimation of the MFD with the available data sources. Most scientific literature has considered the estimation of MFDs based on either loop detector data (LDD) or floating car data (FCD). In this paper, however, we propose a methodology for estimating the MFD based on both data sources simultaneously. To that end, we have defined a fusion algorithm that separates the urban network into two sub-networks, one with loop detectors and one without. The LDD and the FCD are then fused taking into account the accuracy and network coverage of each data type. Simulations of an abstract grid network and the network of the city of Zurich show that the fusion algorithm always reduces the estimation error significantly with respect to an estimation where only one data source is used. This holds true, even when we account for the fact that the probe penetration rate of FCD needs to be estimated with loopHighlights: Improvement of MFD through data fusion of loop detector data and floating car data. Estimation of probe penetration rate through loop detectors. Case studies using simulation of abstract grid network and city of Zurich network. Abstract: A promising framework that describes traffic conditions in urban networks is the macroscopic fundamental diagram (MFD), relating average flow and average density in a relatively homogeneous urban network. It has been shown that the MFD can be used, for example, for traffic access control. However, an implementation requires an accurate estimation of the MFD with the available data sources. Most scientific literature has considered the estimation of MFDs based on either loop detector data (LDD) or floating car data (FCD). In this paper, however, we propose a methodology for estimating the MFD based on both data sources simultaneously. To that end, we have defined a fusion algorithm that separates the urban network into two sub-networks, one with loop detectors and one without. The LDD and the FCD are then fused taking into account the accuracy and network coverage of each data type. Simulations of an abstract grid network and the network of the city of Zurich show that the fusion algorithm always reduces the estimation error significantly with respect to an estimation where only one data source is used. This holds true, even when we account for the fact that the probe penetration rate of FCD needs to be estimated with loop detectors, hence it might also include some errors depending on the number of loop detectors, especially when probe vehicles are not homogeneously distributed within the network. … (more)
- Is Part Of:
- Transportation research. Volume 71(2016)
- Journal:
- Transportation research
- Issue:
- Volume 71(2016)
- Issue Display:
- Volume 71, Issue 2016 (2016)
- Year:
- 2016
- Volume:
- 71
- Issue:
- 2016
- Issue Sort Value:
- 2016-0071-2016-0000
- Page Start:
- 184
- Page End:
- 197
- Publication Date:
- 2016-10
- Subjects:
- MFD estimation -- Simulation -- Loop detector data (LDD) -- Floating car data (FCD) -- Fusion -- Probe penetration estimation
Transportation -- Periodicals
Transportation -- Technological innovations -- Periodicals
388.011 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0968090X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.trc.2016.07.013 ↗
- Languages:
- English
- ISSNs:
- 0968-090X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9026.274620
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 8048.xml