schliessen

Filtern

 

Bibliotheken

Dimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible

This paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse problem is severely ill-posed and hence prior information is required to recover the ground truth. The basic approach in t... Full description

Main Author: Xia, Jingyuan
Other Authors: Dai, Wei , Polak, John , Bierlaire, Michel
Format: Electronic Article Electronic Article
Language:
Subjects:
Quelle: Cornell University
ID: Arxiv ID: 1810.06077
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: arxiv1810.06077
title: Dimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
format: Article
creator:
  • Xia, Jingyuan
  • Dai, Wei
  • Polak, John
  • Bierlaire, Michel
subjects:
  • Computer Science - Computational Engineering, Finance, And Science
  • Computer Science - Numerical Analysis
ispartof:
description: This paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse problem is severely ill-posed and hence prior information is required to recover the ground truth. The basic approach in the literature relies on a forward model where the so called traffic assignment matrix maps OD flows to link flows. Due to the ill-posedness of the problem, prior information on the assignment matrix and OD flows are typically needed. The main contributions of this paper include a dimension reduction of the inquired flows from $O(n^2)$ to $O(n)$, and a demonstration that for the first time the ground truth OD flows can be uniquely identified with no or little prior information. To cope with the ill-posedness due to the large number of unknowns, a new forward model is developed which does not involve OD flows directly but is built upon the flows characterized only by their origins, henceforth referred as O-flows. The new model preserves all the OD information and more importantly reduces the dimension of the inverse problem substantially. A Gauss-Seidel method is deployed to solve the inverse problem, and a necessary condition for the uniqueness of the solution is proved. Simulations demonstrate that blind estimation where no prior information is available is possible for some network settings. Some challenging network settings are identified and discussed, where a remedy based on temporal patterns of the O-flows is developed and numerically shown effective.
language:
source: Cornell University
identifier: Arxiv ID: 1810.06077
fulltext: fulltext_linktorsrc
url: Link


@attributes
ID1159512782
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid1810.06077
sourceidarxiv
recordidTN_arxiv1810.06077
sourcesystemPC
display
typearticle
titleDimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
creatorXia, Jingyuan ; Dai, Wei ; Polak, John ; Bierlaire, Michel
identifierArxiv ID: 1810.06077
subjectComputer Science - Computational Engineering, Finance, And Science ; Computer Science - Numerical Analysis
descriptionThis paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse problem is severely ill-posed and hence prior information is required to recover the ground truth. The basic approach in the literature relies on a forward model where the so called traffic assignment matrix maps OD flows to link flows. Due to the ill-posedness of the problem, prior information on the assignment matrix and OD flows are typically needed. The main contributions of this paper include a dimension reduction of the inquired flows from $O(n^2)$ to $O(n)$, and a demonstration that for the first time the ground truth OD flows can be uniquely identified with no or little prior information. To cope with the ill-posedness due to the large number of unknowns, a new forward model is developed which does not involve OD flows directly but is built upon the flows characterized only by their origins, henceforth referred as O-flows. The new model preserves all the OD information and more importantly reduces the dimension of the inverse problem substantially. A Gauss-Seidel method is deployed to solve the inverse problem, and a necessary condition for the uniqueness of the solution is proved. Simulations demonstrate that blind estimation where no prior information is available is possible for some network settings. Some challenging network settings are identified and discussed, where a remedy based on temporal patterns of the O-flows is developed and numerically shown effective.
sourceCornell University
oafree_for_read
links
openurl$$Topenurl_article
linktorsrc$$Uhttp://arxiv.org/abs/1810.06077$$EView_record_at _arXiv
openurlfulltext$$Topenurlfull_article
search
creatorcontrib
0Xia, Jingyuan
1Dai, Wei
2Polak, John
3Bierlaire, Michel
titleDimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
descriptionThis paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse problem is severely ill-posed and hence prior information is required to recover the ground truth. The basic approach in the literature relies on a forward model where the so called traffic assignment matrix maps OD flows to link flows. Due to the ill-posedness of the problem, prior information on the assignment matrix and OD flows are typically needed. The main contributions of this paper include a dimension reduction of the inquired flows from $O(n^2)$ to $O(n)$, and a demonstration that for the first time the ground truth OD flows can be uniquely identified with no or little prior information. To cope with the ill-posedness due to the large number of unknowns, a new forward model is developed which does not involve OD flows directly but is built upon the flows characterized only by their origins, henceforth referred as O-flows. The new model preserves all the OD information and more importantly reduces the dimension of the inverse problem substantially. A Gauss-Seidel method is deployed to solve the inverse problem, and a necessary condition for the uniqueness of the solution is proved. Simulations demonstrate that blind estimation where no prior information is available is possible for some network settings. Some challenging network settings are identified and discussed, where a remedy based on temporal patterns of the O-flows is developed and numerically shown effective.
subject
0Computer Science - Computational Engineering, Finance, and Science
1Computer Science - Numerical Analysis
general
0Cornell University
1Arxiv ID: 1810.06077
sourceidarxiv
recordidarxiv1810.06077
rsrctypearticle
creationdate
020181014
12018
recordtypearticle
searchscope
0arxiv
1Arxiv
scope
0arxiv
1Arxiv
sort
titleDimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
authorXia, Jingyuan ; Dai, Wei ; Polak, John ; Bierlaire, Michel
creationdate20181014
facets
frbrgroupid-7662883337610767114
frbrtype6
newrecords20181016
creationdate2018
topic
0Computer Science–Computational Engineering, Finance, And Science
1Computer Science–Numerical Analysis
collectionarXiv
prefilterarticles
rsrctypearticles
creatorcontrib
0Xia, Jingyuan
1Dai, Wei
2Polak, John
3Bierlaire, Michel
delivery
delcategoryRemote Search Resource
fulltextfulltext_linktorsrc
addata
aulast
0Xia
1Dai
2Polak
3Bierlaire
aufirst
0Jingyuan
1Wei
2John
3Michel
au
0Xia, Jingyuan
1Dai, Wei
2Polak, John
3Bierlaire, Michel
atitleDimension Reduction for Origin-Destination Flow Estimation: Blind Estimation Made Possible
risdate20181014
genrearticle
ristypeJOUR
abstractThis paper studies the problem of estimating origin-destination (OD) flows from link flows. As the number of link flows is typically much less than that of OD flows, the inverse problem is severely ill-posed and hence prior information is required to recover the ground truth. The basic approach in the literature relies on a forward model where the so called traffic assignment matrix maps OD flows to link flows. Due to the ill-posedness of the problem, prior information on the assignment matrix and OD flows are typically needed. The main contributions of this paper include a dimension reduction of the inquired flows from $O(n^2)$ to $O(n)$, and a demonstration that for the first time the ground truth OD flows can be uniquely identified with no or little prior information. To cope with the ill-posedness due to the large number of unknowns, a new forward model is developed which does not involve OD flows directly but is built upon the flows characterized only by their origins, henceforth referred as O-flows. The new model preserves all the OD information and more importantly reduces the dimension of the inverse problem substantially. A Gauss-Seidel method is deployed to solve the inverse problem, and a necessary condition for the uniqueness of the solution is proved. Simulations demonstrate that blind estimation where no prior information is available is possible for some network settings. Some challenging network settings are identified and discussed, where a remedy based on temporal patterns of the O-flows is developed and numerically shown effective.
lad21arXiv.org:1810.06077
oafree_for_read
date2018-10-14