An Online Change-Point-Based Model for Traffic Parameter Prediction

This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and density are subject to shifts because of weather, accidents, driving characteristics, etc. An intuitive approach of employing the hidden Marko...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on intelligent transportation systems Vol. 14; no. 3; pp. 1360 - 1369
Main Authors Comert, Gurcan, Bezuglov, Anton
Format Journal Article
LanguageEnglish
Published IEEE 01.09.2013
Subjects
Adaptation models
Change point models
Data models
Forecasting
hidden Markov model (HMM)
Hidden Markov models
Numerical models
Prediction algorithms
Predictive models
time-series autoregressive integrated moving average (ARIMA)
traffic prediction
Online AccessGet full text

Cover

Abstract This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and density are subject to shifts because of weather, accidents, driving characteristics, etc. An intuitive approach of employing the hidden Markov model (HMM) and the expectation-maximization (EM) algorithm as change point models at these shifts and accordingly adapting the autoregressive-integrated-moving-average (ARIMA) forecasting model is formulated. The model is fitted and tested using publicly available 1993 I-880 loop data. It is compared with basic and mean updating forecasting models. Detailed numerical experiments are given on several days of data to show the impact of using change point models for adaptive forecasting models.
AbstractList This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and density are subject to shifts because of weather, accidents, driving characteristics, etc. An intuitive approach of employing the hidden Markov model (HMM) and the expectation-maximization (EM) algorithm as change point models at these shifts and accordingly adapting the autoregressive-integrated-moving-average (ARIMA) forecasting model is formulated. The model is fitted and tested using publicly available 1993 I-880 loop data. It is compared with basic and mean updating forecasting models. Detailed numerical experiments are given on several days of data to show the impact of using change point models for adaptive forecasting models.
Author Bezuglov, Anton
Comert, Gurcan
Author_xml – sequence: 1
  givenname: Gurcan
  surname: Comert
  fullname: Comert, Gurcan
  email: comertg@benedict.edu
  organization: Phys. & Eng. Dept., Benedict Coll., Columbia, SC, USA
– sequence: 2
  givenname: Anton
  surname: Bezuglov
  fullname: Bezuglov, Anton
  email: bezuglova@benedict.edu
  organization: Math & Comput. Sci. Dept., Benedict Coll., Columbia, SC, USA
BookMark eNp9kMtOwzAQRS1UJNrCByA2_gEXjxPHybJEvKSiViKso4kzBqPUQU42_D2NWrFgwWpGI525V2fBZqEPxNg1yBWALG6r5-p1pSQkK6UyqVN5xuagdS6khGw27SoVhdTygi2G4fNwTTXAnJXrwLeh84F4-YHhncSu92EUdzhQy1_6ljru-siriM55y3cYcU8jRb6L1Ho7-j5csnOH3UBXp7lkbw_3VfkkNtvH53K9EVZlehRAlGctUtGqrEgsGSON0ya3CLaRpmiMlWmDqdWZhbRJ8iTB3BSonAZrG0yWDI5_beyHIZKrv6LfY_yuQdaThXqyUE8W6pOFA2P-MNaPOLUeI_ruX_LmSHoi-k3KtFJg8uQH1qFr_A
CODEN ITISFG
CitedBy_id crossref_primary_10_1007_s40998_019_00227_1
crossref_primary_10_1109_TITS_2019_2939290
crossref_primary_10_1016_j_neucom_2017_03_049
crossref_primary_10_1109_TITS_2018_2873137
crossref_primary_10_1016_j_procs_2014_05_435
crossref_primary_10_3390_s20030685
crossref_primary_10_1016_j_rser_2022_112947
crossref_primary_10_1186_1687_1499_2014_85
crossref_primary_10_1109_TITS_2015_2457240
crossref_primary_10_1080_23311916_2016_1270703
crossref_primary_10_1109_TITS_2024_3411638
crossref_primary_10_1109_TCCN_2020_3018157
crossref_primary_10_3390_math11163574
crossref_primary_10_1016_j_vehcom_2019_100184
crossref_primary_10_1016_j_knosys_2019_01_015
crossref_primary_10_1177_03611981241308868
crossref_primary_10_1109_TITS_2023_3306559
crossref_primary_10_1007_s40534_019_0193_2
crossref_primary_10_1007_s11071_024_09844_w
crossref_primary_10_1109_ACCESS_2018_2879055
crossref_primary_10_1049_itr2_12127
crossref_primary_10_1049_iet_its_2018_5385
crossref_primary_10_1080_15472450_2022_2069498
crossref_primary_10_1109_ACCESS_2018_2805299
crossref_primary_10_1109_TITS_2022_3161623
crossref_primary_10_1109_ACCESS_2020_3018452
crossref_primary_10_1109_TITS_2014_2345663
crossref_primary_10_1142_S0129183117501212
crossref_primary_10_1007_s11063_018_9804_x
crossref_primary_10_1016_j_physa_2019_122601
crossref_primary_10_1155_2019_5314520
crossref_primary_10_1016_j_oceaneng_2023_115687
crossref_primary_10_1038_s41598_025_93168_1
crossref_primary_10_32604_cmc_2022_027707
crossref_primary_10_3390_app10010356
crossref_primary_10_1109_ACCESS_2020_2970250
crossref_primary_10_1061_JTEPBS_0000654
crossref_primary_10_1016_j_ress_2020_107126
crossref_primary_10_1016_j_physa_2020_125574
crossref_primary_10_3390_app13042143
crossref_primary_10_3390_s21155074
crossref_primary_10_1109_TITS_2020_3037791
crossref_primary_10_1109_MITS_2014_2332591
crossref_primary_10_1111_mice_12381
crossref_primary_10_1049_iet_its_2019_0133
crossref_primary_10_1109_ACCESS_2019_2938236
crossref_primary_10_1016_j_aej_2023_05_015
crossref_primary_10_1016_j_trc_2016_09_014
crossref_primary_10_3390_math12121895
crossref_primary_10_1109_JIOT_2022_3196461
crossref_primary_10_1109_TVT_2023_3239054
crossref_primary_10_1016_j_engappai_2023_107012
crossref_primary_10_1109_COMST_2023_3273121
Cites_doi 10.1016/j.trc.2005.04.007
10.1109/TITS.2011.2174634
10.3141/1776-25
10.1080/0144164042000195072
10.1007/s00477-004-0224-2
10.3141/1934-07
10.1016/j.trc.2009.11.001
10.1016/S0169-2070(96)00700-5
10.3141/2175-04
10.1061/(ASCE)0733-947X(2006)132:2(114)
10.1090/S0002-9904-1967-11751-8
10.1093/comjnl/bxq003
10.1109/TITS.2009.2021448
10.1109/TITS.2011.2174051
10.1109/TITS.2004.833765
10.1016/j.trc.2010.10.004
10.1109/5.18626
10.1109/TITS.2011.2173933
10.1016/S0968-090X(03)00004-4
10.1214/aoms/1177697196
10.1109/TITS.2008.2011693
10.1016/S0968-090X(02)00009-8
10.3141/1879-01
10.1016/0191-2615(84)90002-X
10.1002/0471721182
10.3141/1710-06
10.1016/S0968-090X(03)00026-3
10.1111/j.2517-6161.1977.tb01600.x
10.1016/j.csda.2011.11.002
10.1016/j.trc.2007.11.003
10.1111/j.1467-8667.2009.00620.x
10.1175/1520-0442(2002)015<2547:DOUCAR>2.0.CO;2
10.1016/j.ijforecast.2010.09.001
10.1016/j.trc.2009.04.006
10.1061/(ASCE)0733-947X(1997)123:4(261)
10.1109/TITS.2009.2028149
10.1016/S0165-0270(02)00275-3
10.1007/s00180-007-0097-1
10.1016/j.trb.2004.03.003
10.1016/j.trc.2010.10.002
10.1093/biomet/59.3.539
10.1093/oxfordhb/9780195398649.001.0001
10.3141/1783-08
10.3141/1876-09
10.1623/hysj.52.1.18
10.1061/(ASCE)0733-947X(2003)129:6(664)
10.3141/1965-03
10.1093/biomet/asq013
10.3141/1993-05
10.3141/1811-04
10.1016/j.trc.2009.03.001
10.1016/j.ijforecast.2003.10.004
10.1002/(SICI)1099-1255(199609)11:5<475::AID-JAE409>3.0.CO;2-9
10.3141/1923-03
ContentType Journal Article
DBID 97E
RIA
RIE
AAYXX
CITATION
DOI 10.1109/TITS.2013.2260540
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library
  url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1558-0016
EndPage 1369
ExternalDocumentID 10_1109_TITS_2013_2260540
6522178
Genre orig-research
GroupedDBID -~X
0R~
29I
4.4
5GY
5VS
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACGFO
ACGFS
ACIWK
ACNCT
AENEX
AETIX
AGQYO
AGSQL
AHBIQ
AIBXA
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CS3
DU5
EBS
EJD
HZ~
H~9
IFIPE
IPLJI
JAVBF
LAI
M43
O9-
OCL
P2P
PQQKQ
RIA
RIE
RNS
ZY4
AAYXX
CITATION
RIG
ID FETCH-LOGICAL-c265t-1ee86dae9d2693ce7707f578ca1cb079b7c04ba4c56c14b3833a879a2f51ccba3
IEDL.DBID RIE
ISSN 1524-9050
IngestDate Thu Apr 24 23:02:56 EDT 2025
Tue Jul 01 04:28:57 EDT 2025
Tue Aug 26 16:44:37 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 3
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c265t-1ee86dae9d2693ce7707f578ca1cb079b7c04ba4c56c14b3833a879a2f51ccba3
PageCount 10
ParticipantIDs crossref_primary_10_1109_TITS_2013_2260540
crossref_citationtrail_10_1109_TITS_2013_2260540
ieee_primary_6522178
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2013-09-01
PublicationDateYYYYMMDD 2013-09-01
PublicationDate_xml – month: 09
  year: 2013
  text: 2013-09-01
  day: 01
PublicationDecade 2010
PublicationTitle IEEE transactions on intelligent transportation systems
PublicationTitleAbbrev TITS
PublicationYear 2013
Publisher IEEE
Publisher_xml – name: IEEE
References ref57
ref13
castle (ref43) 2011
ref12
ref59
ref58
ref14
ref52
ref11
ref10
ref16
ref19
ref51
ref50
ref45
ref47
ref42
ref41
adams (ref56) 2007
ref49
shen (ref15) 2010; 1
ref8
ref9
ref4
qin (ref31) 2004; 1876
ref3
ref6
ref5
ref40
hastie (ref63) 2009
vlahogianni (ref18) 2011; 19
ref35
ref34
ref37
ref36
ref30
turner (ref53) 2010; 1
ref32
turner (ref55) 2009; 1
dempster (ref62) 1977; 39
mclachlan (ref64) 2000
ref2
ref1
lee (ref7) 2010; 1
ref39
ref38
hamilton (ref46) 2008
qi (ref33) 2010
brook (ref66) 1972; 59
ref24
ref67
ref23
ref26
ref25
ref20
ref22
ref65
ref21
ref28
ref29
zwet (ref27) 2004; 5
ref60
saatci (ref54) 2010; 1
ref61
aiolfi (ref44) 2011
zhang (ref17) 2010; 1
giron (ref48) 2007; 8
References_xml – year: 2010
  ident: ref33
  publication-title: Probabilistic Models for Short Term Traffic Conditions Prediction
– ident: ref14
  doi: 10.1016/j.trc.2005.04.007
– ident: ref8
  doi: 10.1109/TITS.2011.2174634
– ident: ref67
  doi: 10.3141/1776-25
– year: 2009
  ident: ref63
  publication-title: The Elements of Statistical Learning Data Mining Inference and Prediction
– volume: 1
  start-page: 1
  year: 2010
  ident: ref7
  article-title: Comparison of univariate and multivariate time series models in short-term freeway traffic flow forecasting
  publication-title: Proc TRB 89th Annu Meeting Compend Papers DVD
– ident: ref22
  doi: 10.1080/0144164042000195072
– ident: ref47
  doi: 10.1007/s00477-004-0224-2
– ident: ref24
  doi: 10.3141/1934-07
– ident: ref25
  doi: 10.1016/j.trc.2009.11.001
– ident: ref9
  doi: 10.1016/S0169-2070(96)00700-5
– start-page: 271
  year: 2011
  ident: ref43
  publication-title: The Oxford Handbook of Economic Forecasting
– ident: ref39
  doi: 10.3141/2175-04
– year: 2007
  ident: ref56
  publication-title: Bayesian Online Change-point Detection
– ident: ref19
  doi: 10.1061/(ASCE)0733-947X(2006)132:2(114)
– volume: 1
  start-page: 927
  year: 2010
  ident: ref54
  article-title: Gaussian process change point models
  publication-title: Proc Int Conf Mach Learn
– ident: ref60
  doi: 10.1090/S0002-9904-1967-11751-8
– ident: ref57
  doi: 10.1093/comjnl/bxq003
– year: 2008
  ident: ref46
  publication-title: Regime Switching Models
– ident: ref5
  doi: 10.1109/TITS.2009.2021448
– ident: ref20
  doi: 10.1109/TITS.2011.2174051
– volume: 5
  start-page: 200
  year: 2004
  ident: ref27
  article-title: A simple and effective method for predicting travel times on freeways
  publication-title: IEEE Trans Intell Transp Syst
  doi: 10.1109/TITS.2004.833765
– volume: 19
  start-page: 387
  year: 2011
  ident: ref18
  article-title: Statistical methods versus neural networks in transportation research: Differences, similarities, and some insights
  publication-title: Transp Res C Emerg Technol
  doi: 10.1016/j.trc.2010.10.004
– ident: ref59
  doi: 10.1109/5.18626
– volume: 1
  start-page: 1
  year: 2010
  ident: ref17
  article-title: Intelligent intersection traffic flow prediction based on fuzzy neutral network
  publication-title: Proc TRB 89th Annu Meeting Compend Papers DVD
– ident: ref21
  doi: 10.1109/TITS.2011.2173933
– ident: ref3
  doi: 10.1016/S0968-090X(03)00004-4
– ident: ref61
  doi: 10.1214/aoms/1177697196
– ident: ref36
  doi: 10.1109/TITS.2008.2011693
– ident: ref2
  doi: 10.1016/S0968-090X(02)00009-8
– ident: ref11
  doi: 10.3141/1879-01
– ident: ref13
  doi: 10.1016/0191-2615(84)90002-X
– start-page: 355
  year: 2011
  ident: ref44
  publication-title: The Oxford Handbook of Economic Forecasting
– year: 2000
  ident: ref64
  publication-title: Finite Mixture Models
  doi: 10.1002/0471721182
– ident: ref29
  doi: 10.3141/1710-06
– ident: ref28
  doi: 10.1016/S0968-090X(03)00026-3
– volume: 39
  start-page: 1
  year: 1977
  ident: ref62
  article-title: Maximum likelihood from incomplete data via the EM algorithm
  publication-title: J Roy Stat Soc B
  doi: 10.1111/j.2517-6161.1977.tb01600.x
– ident: ref65
  doi: 10.1016/j.csda.2011.11.002
– ident: ref34
  doi: 10.1016/j.trc.2007.11.003
– ident: ref16
  doi: 10.1111/j.1467-8667.2009.00620.x
– ident: ref50
  doi: 10.1175/1520-0442(2002)015<2547:DOUCAR>2.0.CO;2
– ident: ref45
  doi: 10.1016/j.ijforecast.2010.09.001
– ident: ref12
  doi: 10.1016/j.trc.2009.04.006
– ident: ref23
  doi: 10.1061/(ASCE)0733-947X(1997)123:4(261)
– volume: 1
  start-page: 1
  year: 2010
  ident: ref15
  article-title: Freeway travel time prediction with dynamic neural networks
  publication-title: Proc TRB 89th Annu Meeting Compend Papers DVD
– ident: ref6
  doi: 10.1109/TITS.2009.2028149
– ident: ref51
  doi: 10.1016/S0165-0270(02)00275-3
– ident: ref58
  doi: 10.1007/s00180-007-0097-1
– ident: ref10
  doi: 10.1016/j.trb.2004.03.003
– ident: ref35
  doi: 10.1016/j.trc.2010.10.002
– volume: 59
  start-page: 539
  year: 1972
  ident: ref66
  article-title: An approach to the probability distribution of CUSUM run length
  publication-title: Biometrika
  doi: 10.1093/biomet/59.3.539
– volume: 1
  start-page: 213
  year: 2010
  ident: ref53
  article-title: Bayesian change point detection for satellite fault prediction
  publication-title: Proc of IGCC
– volume: 1
  start-page: 1
  year: 2009
  ident: ref55
  article-title: Adaptive sequential Bayesian change point detection
  publication-title: Proc Temp Segment Workshop NIPS
– ident: ref42
  doi: 10.1093/oxfordhb/9780195398649.001.0001
– ident: ref30
  doi: 10.3141/1783-08
– volume: 1876
  start-page: 82
  year: 2004
  ident: ref31
  article-title: Adaptive calibration of dynamic speed?density relations for online network traffic estimation and prediction applications
  publication-title: Transp Res Rec J Transp Res Board
  doi: 10.3141/1876-09
– ident: ref52
  doi: 10.1623/hysj.52.1.18
– ident: ref4
  doi: 10.1061/(ASCE)0733-947X(2003)129:6(664)
– volume: 8
  start-page: 1
  year: 2007
  ident: ref48
  article-title: Objective Bayesian analysis of multiple change points for linear models
  publication-title: Proc Bayesian Stats
– ident: ref32
  doi: 10.3141/1965-03
– ident: ref49
  doi: 10.1093/biomet/asq013
– ident: ref37
  doi: 10.3141/1993-05
– ident: ref26
  doi: 10.3141/1811-04
– ident: ref40
  doi: 10.1016/j.trc.2009.03.001
– ident: ref1
  doi: 10.1016/j.ijforecast.2003.10.004
– ident: ref41
  doi: 10.1002/(SICI)1099-1255(199609)11:5<475::AID-JAE409>3.0.CO;2-9
– ident: ref38
  doi: 10.3141/1923-03
SSID ssj0014511
Score 2.3385067
Snippet This paper develops a method for predicting traffic parameters under abrupt changes based on change point models. Traffic parameters such as speed, flow, and...
SourceID crossref
ieee
SourceType Enrichment Source
Index Database
Publisher
StartPage 1360
SubjectTerms Adaptation models
Change point models
Data models
Forecasting
hidden Markov model (HMM)
Hidden Markov models
Numerical models
Prediction algorithms
Predictive models
time-series autoregressive integrated moving average (ARIMA)
traffic prediction
Title An Online Change-Point-Based Model for Traffic Parameter Prediction
URI https://ieeexplore.ieee.org/document/6522178
Volume 14
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LSwMxEA5tT3rwVcX6IgdPYtrsbh6bYxVLFSoFW-htSbJZEMtWyvbirzezu12KiHgLYQLDTMLMZGa-Qeg2E6kRYZqSTDNDAOKcxJwbIoRWcew87waakyevYjxnLwu-aKH7phfGOVcWn7k-LMtcfrqyG_gqGwjvLAQybqO2D9yqXq0mYwA4WyU2asiIonybwQyoGsyeZ29QxBX1Q_De4Z9jxwbtDFUpbcroEE223FSlJB_9TWH69usHUON_2T1CB7VziYfVbThGLZefoP0dyMEuehzmuIIXxVVjAZmu3vOCPHhrlmKYjLbE3o_F3oYBuASeaqje8sLH0zXkdECPp2g-epo9jkk9SIHYUPCCBM7FItVOpaFQkXVSUpn5p2p1YA2VykhLmdHMcmEDZnzQGulYKh1mPLDW6OgMdfJV7s4R9qRcZi7ixnhBahqrlGbaZBFj2vqNHqJb0Sa2RhmHYRfLpIw2qEpAGwloI6m10UN3zZHPCmLjL-IuCLohrGV88fv2JdoLy_EVUBN2hTrFeuOuvRNRmJvy9nwDcArDTg
linkProvider IEEE
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LSwMxEB58HNSDryq-zcGTmDb7SHZz1KJUbUvBCr0tSTYLYtmKbC_-ejO726WIiLclzMIwkzCTzMz3AVxlItXCT1OaqVBThDinMeeaCqFkHFunu8bh5MFQ9F7DpwmfrMBNMwtjrS2bz2wbP8tafjozc3wq6wiXLHhRvArrLu5zr5rWamoGiLRVoqP6IZWML2qYHpOd8eP4Bdu4graP-Tu-dCxFoSValTKqPOzAYKFP1Uzy3p4Xum2-fkA1_lfhXdiu00tyW-2HPVix-T5sLYEOtqB7m5MKYJRUowV0NHvLC3rn4llKkBttSlwmS1wUQ3gJMlLYv-XMT0afWNVBTx7A68P9uNujNZUCNb7gBfWsjUWqrEx9IQNjo4hFmTusRnlGs0jqyLBQq9BwYbxQu2troOJIKj_jnjFaBYewls9yewTEifIoswHX2hlSsVimLFM6C8JQGbdwDGxh2sTUOONIdzFNyvsGkwl6I0FvJLU3juG6-eWjAtn4S7iFhm4Eaxuf_L58CRu98aCf9B-Hz6ew6ZdkFtghdgZrxefcnruUotAX5U76BtAsxpc
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=An+Online+Change-Point-Based+Model+for+Traffic+Parameter+Prediction&rft.jtitle=IEEE+transactions+on+intelligent+transportation+systems&rft.au=Comert%2C+Gurcan&rft.au=Bezuglov%2C+Anton&rft.date=2013-09-01&rft.issn=1524-9050&rft.eissn=1558-0016&rft.volume=14&rft.issue=3&rft.spage=1360&rft.epage=1369&rft_id=info:doi/10.1109%2FTITS.2013.2260540&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TITS_2013_2260540
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1524-9050&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1524-9050&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1524-9050&client=summon