Loading…

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

•A method for increased accuracy of link travel time estimation from sparse FCD.•A fixed point formulation of the path inference and travel time estimation problem.•Iterations converge quickly to solution with consistent paths and travel times.•Validation shows fixed point algorithm improves shortes...

Full description

Saved in:
Bibliographic Details
Published in:Transportation research. Part C, Emerging technologies Emerging technologies, 2017-12, Vol.85, p.628-643
Main Authors: Rahmani, Mahmood, Koutsopoulos, Haris N., Jenelius, Erik
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3
cites cdi_FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3
container_end_page 643
container_issue
container_start_page 628
container_title Transportation research. Part C, Emerging technologies
container_volume 85
creator Rahmani, Mahmood
Koutsopoulos, Haris N.
Jenelius, Erik
description •A method for increased accuracy of link travel time estimation from sparse FCD.•A fixed point formulation of the path inference and travel time estimation problem.•Iterations converge quickly to solution with consistent paths and travel times.•Validation shows fixed point algorithm improves shortest path finding.•Impact can be significant for links representing local and side streets. Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.
doi_str_mv 10.1016/j.trc.2017.10.012
format article
fullrecord <record><control><sourceid>elsevier_swepu</sourceid><recordid>TN_cdi_swepub_primary_oai_DiVA_org_kth_218115</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0968090X17302863</els_id><sourcerecordid>S0968090X17302863</sourcerecordid><originalsourceid>FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3</originalsourceid><addsrcrecordid>eNp9UMtOwzAQtBBIlMIHcPMPpHjjtLHhVJWnhMSlIG6W46xb9xFHtqHw97gq4shpNLMzK80QcglsBAwmV6tRCmZUMqgzHzEoj8gARC2Lko_lMRkwOREFk-z9lJzFuGKMgRzXA7KeB_2JG5rcFinGDDo531Eb_JbGXoeI1G58FrsFNTrQVidNdy4tqfFddDFhl2ivM3edxYCdwWs6pdZ9YUt77_JV933w2izPyYnVm4gXvzgkr_d389lj8fzy8DSbPhemAkgFVKIRTYuVFNI0vLYCZMVLblshcdwANNxCOamyIngtbJM1rtuK1RY41JoPSXH4G3fYfzSqD7lV-FZeO3Xr3qbKh4Vap6UqQQCMsx8OfhN8jAHtXwKY2q-rViqvq_br7qW8bs7cHDKYi3w6DCoaty_fuoAmqda7f9I_ILGEHA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach</title><source>ScienceDirect Journals</source><creator>Rahmani, Mahmood ; Koutsopoulos, Haris N. ; Jenelius, Erik</creator><creatorcontrib>Rahmani, Mahmood ; Koutsopoulos, Haris N. ; Jenelius, Erik</creatorcontrib><description>•A method for increased accuracy of link travel time estimation from sparse FCD.•A fixed point formulation of the path inference and travel time estimation problem.•Iterations converge quickly to solution with consistent paths and travel times.•Validation shows fixed point algorithm improves shortest path finding.•Impact can be significant for links representing local and side streets. Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.</description><identifier>ISSN: 0968-090X</identifier><identifier>ISSN: 1879-2359</identifier><identifier>EISSN: 1879-2359</identifier><identifier>DOI: 10.1016/j.trc.2017.10.012</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Fixed point problem ; Floating car data ; Path inference ; Travel time estimation</subject><ispartof>Transportation research. Part C, Emerging technologies, 2017-12, Vol.85, p.628-643</ispartof><rights>2017 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3</citedby><cites>FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3</cites><orcidid>0000-0002-4106-3126</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27924,27925</link.rule.ids><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-218115$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Rahmani, Mahmood</creatorcontrib><creatorcontrib>Koutsopoulos, Haris N.</creatorcontrib><creatorcontrib>Jenelius, Erik</creatorcontrib><title>Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach</title><title>Transportation research. Part C, Emerging technologies</title><description>•A method for increased accuracy of link travel time estimation from sparse FCD.•A fixed point formulation of the path inference and travel time estimation problem.•Iterations converge quickly to solution with consistent paths and travel times.•Validation shows fixed point algorithm improves shortest path finding.•Impact can be significant for links representing local and side streets. Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.</description><subject>Fixed point problem</subject><subject>Floating car data</subject><subject>Path inference</subject><subject>Travel time estimation</subject><issn>0968-090X</issn><issn>1879-2359</issn><issn>1879-2359</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9UMtOwzAQtBBIlMIHcPMPpHjjtLHhVJWnhMSlIG6W46xb9xFHtqHw97gq4shpNLMzK80QcglsBAwmV6tRCmZUMqgzHzEoj8gARC2Lko_lMRkwOREFk-z9lJzFuGKMgRzXA7KeB_2JG5rcFinGDDo531Eb_JbGXoeI1G58FrsFNTrQVidNdy4tqfFddDFhl2ivM3edxYCdwWs6pdZ9YUt77_JV933w2izPyYnVm4gXvzgkr_d389lj8fzy8DSbPhemAkgFVKIRTYuVFNI0vLYCZMVLblshcdwANNxCOamyIngtbJM1rtuK1RY41JoPSXH4G3fYfzSqD7lV-FZeO3Xr3qbKh4Vap6UqQQCMsx8OfhN8jAHtXwKY2q-rViqvq_br7qW8bs7cHDKYi3w6DCoaty_fuoAmqda7f9I_ILGEHA</recordid><startdate>20171201</startdate><enddate>20171201</enddate><creator>Rahmani, Mahmood</creator><creator>Koutsopoulos, Haris N.</creator><creator>Jenelius, Erik</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8V</scope><orcidid>https://orcid.org/0000-0002-4106-3126</orcidid></search><sort><creationdate>20171201</creationdate><title>Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach</title><author>Rahmani, Mahmood ; Koutsopoulos, Haris N. ; Jenelius, Erik</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Fixed point problem</topic><topic>Floating car data</topic><topic>Path inference</topic><topic>Travel time estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rahmani, Mahmood</creatorcontrib><creatorcontrib>Koutsopoulos, Haris N.</creatorcontrib><creatorcontrib>Jenelius, Erik</creatorcontrib><collection>CrossRef</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Kungliga Tekniska Högskolan</collection><jtitle>Transportation research. Part C, Emerging technologies</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rahmani, Mahmood</au><au>Koutsopoulos, Haris N.</au><au>Jenelius, Erik</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach</atitle><jtitle>Transportation research. Part C, Emerging technologies</jtitle><date>2017-12-01</date><risdate>2017</risdate><volume>85</volume><spage>628</spage><epage>643</epage><pages>628-643</pages><issn>0968-090X</issn><issn>1879-2359</issn><eissn>1879-2359</eissn><abstract>•A method for increased accuracy of link travel time estimation from sparse FCD.•A fixed point formulation of the path inference and travel time estimation problem.•Iterations converge quickly to solution with consistent paths and travel times.•Validation shows fixed point algorithm improves shortest path finding.•Impact can be significant for links representing local and side streets. Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.trc.2017.10.012</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-4106-3126</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0968-090X
ispartof Transportation research. Part C, Emerging technologies, 2017-12, Vol.85, p.628-643
issn 0968-090X
1879-2359
1879-2359
language eng
recordid cdi_swepub_primary_oai_DiVA_org_kth_218115
source ScienceDirect Journals
subjects Fixed point problem
Floating car data
Path inference
Travel time estimation
title Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T17%3A39%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-elsevier_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Travel%20time%20estimation%20from%20sparse%20floating%20car%20data%20with%20consistent%20path%20inference:%20A%20fixed%20point%20approach&rft.jtitle=Transportation%20research.%20Part%20C,%20Emerging%20technologies&rft.au=Rahmani,%20Mahmood&rft.date=2017-12-01&rft.volume=85&rft.spage=628&rft.epage=643&rft.pages=628-643&rft.issn=0968-090X&rft.eissn=1879-2359&rft_id=info:doi/10.1016/j.trc.2017.10.012&rft_dat=%3Celsevier_swepu%3ES0968090X17302863%3C/elsevier_swepu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c411t-148b8bde4989cb37f8194323fd89e5b11b3f126423f8378fb5b13ad407f1317a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true