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Vehicle Location System Based on RFID

Authors:
Fawzi Mohammed Munir Al-Naima at Nahrain University
Fawzi Mohammed Munir Al-Naima
Hassan Abdulsalam Hamid at Northern Technical University
Hassan Abdulsalam Hamid
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Abstract

Due to the rapid worldwide growth in the number of vehicles on the road, traffic problems are bound to exist. Hence, the implementation of Intelligent Transportation Systems (ITS) to obtain traffic information from roads is becoming an urgent necessity. This paper tackles the problem of designing a Vehicle Location System (VLS) based on Radio Frequency Identification (RFID). The proposed system consists of passive RFID tags on vehicles, RFID readers, wireless Ethernet communication with a Central Computer System (CCS) and commanding software. The gathered information will be filtered and stored in a suitable form to be easily used in many useful applications. Such applications include, but not limited to, location of vehicles in intersections at any time, path and orientation of vehicle in intersections, the numbers and the IDs of vehicles passing each intersection at any time, traffic information statistics, estimate the traffic congestion situation in roads, etc.
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International Journal of Engineering Business Management
Special Issue on Radio Frequency Identification and Wireless Sensor Networks
Editor: Cristina Turcu
Vehicle Traffic Congestion
Estimation Based on RFID
Regular Paper
Fawzi M. Al-Naima1,* and Hassan A. Hamd2
1 Department of Computer Engineering, Nahrain University, Baghdad, Iraq
2 Department of Computer Engineering, Nahrain University, Baghdad, Iraq
* Corresponding author E-mail: fawzi.alnaima@ieee.org
Originally published in the International Journal of Radio Frequency Identification & Wireless Sensor Networks, ISSN 1847-9812
© 2012 Al-Naima and Hamd; licensee InTech. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
AbstractDuetotheproliferationinthenumberof
vehiclesontheroad,trafficproblemsareboundtoexist.
Therefore,theuseofIntelligentTransportationSystems
(ITS)hasbecomemandatoryforobtainingtraffic
informationfromroads.RadioFrequencyIdentification
(RFID)technologyhasbeenusedtoobtainvehicles’IDs
(tagID)fromRFIDreadersandtocollecttraffic
informationinreal‐time.Thispaperproposesa
simulationsystemfortheVehicleTrafficCongestion
Estimation(VTCE)basedonRFID.TheRFIDreaderwill
readthevehicletagsandtransferthenecessary
informationtoadatabaseinaCentralComputerSystem
(CCS).TheCCSutilizesthesedatatodeterminethetraffic
congestionstatusoftheroadnetworkbyfollowinga
specificprocedure.
KeywordsRFID,Trafficcongestion,Intelligent
TransportationSystems.
1.Introduction
Therapidincreaseinroadtrafficappearstobeoneof
themajorproblemsfacingurbanandsub‐urbanareasin
recentyears.Trafficcongestionandjamsareoneofthe
mainreasonsforimmenselyincreasingtransportation
costsduetothewastedtimeandextrafuel.Traffic
reportsinreal‐timeareessentialforalleviating
congestioninovercrowdedcities,aswellasallowing
commutersthechoiceofproperroutestoavoid
becomingstuckintrafficforhours.Severalinitiatives
havebeenproposedandimplementedtogathertraffic
datatofeedtheITS[1].Accordingtooursurvey,most
effortsfocusonlimitedinstallationoffixedsensors
suchasloop‐coilsandintelligentvideocameraswith
imageprocessingcapability.However,thecostsof
suchimplementationsareveryhighduetothehigh
costofthedevices,installationandmaintenance.
Moreover,thesefixedsensorsarevulnerableto
extremeweatherincertainareas.Analternativeway
tocollecttrafficdataatalowercostwithwider
coverageisthereforeneeded[1].
Evenifdriversattempttoavoiddailyrushhourtraffic,
thecircumnavigationofallsuddenlyarisingtraffic
congestionismostlyimpossiblewithcurrenttraffic
managementsystems.Tomanagethischallengeinthe
future,theU.S.FederalHighwayAdministration
(FHWA)identifiedthreegeneralapproachestoreduce
trafficcongestion[2]:
Extensionofthecurrentbasecapacity,e.g.,increase
ofthenumberandsizeofhighways.
1
Fawzi M. Al-Naima, and Hassan A. Hamd:
Vehicle Traffic Congestion Estimation Based on RFID
www.intechopen.com
ARTICLE
www.intechopen.com Int. j. eng. bus. manag., 2012, Vol. 4, 30:2012
Encouragementofalternativetravelandlanduse
conceptsthatrequirefewerresources,e.g.,non‐
automotivetravelmodes.
Usingtheexistingcapacitiesmoreefficiently.
Theapproachproposedinthispaperfocusesonthethird
strategy.AsstatedintheFHWAfinalreport[2],allof
thesethreestrategiesreducetrafficcongestion,but
strategiesforamoreefficientuseofexistingcapacities
havethemosteffectiveimpact,sincetheyconcentrateon
thebasiccauseoftheproblemsinsteadofonlyalleviating
thenegativeeffects.
RFIDtechnologyisoneofthemostrapidlygrowing
segmentsoftodayʹsautomaticidentificationdata
collection(AIDC)industry[3]. ʹʹRFIDtagsʹʹonobjectsor
assets,and ʹʹRFIDreadersʹʹ areusedtogatherthetag
information.RFIDrepresentsanimprovementonbar
codesintermsofnon‐opticalcommunication[4].The
VTCEsystembasedonRFIDisdesignedforalllegally
registeredvehicleswhichmustholdRFIDtags.When
thesevehiclestravelalongaroadorintersectioninwhich
aVTCEsystemisinstalled[5][6],thevehicletag
informationisreadandsentimmediatelytotheCCSunit
forthepurposesofreal‐timemonitoringand
managementofvehiclemovementconditions.

Thispaperisorganizedasfollows:section2introduces
theprinciplesofRFID.Section3describestheVTCE
system.Section4discussesthesoftwareofVTCE.Section
5describesthesimulationofroadsandtraffic
intersections.Section6discussesthetrafficcongestion
estimationstrategy.Section7givestheapplicationof
VTCE,andfinally,theconclusionisgiveninsection8.
2.PrinciplesofRFIDTechnology
RFIDisnotanewtechnology,forexample,theprinciples
ofRFIDwereemployedbytheBritishinWorldWarIIto
identifytheiraircraftusingtheIFFsystem(Identity:
FriendorFoe)[7].TodayRFIDisagenerictermfor
technologiesthatuseradiowavestoautomatically
identifypeopleorobjects[4].AtypicalRFIDsystemwill
compriseRFIDtags,RFIDreaderandmiddleware,see
Fig.1.
Figure1.ComponentsofanRFIDsystem
EachRFIDtaghasauniqueserialcodeorID,andplaced
inorattachedtotheobjecttobeidentified.Itcontains
informationonthebasicpropertiesoftheobject[8].An
RFIDtagrespondstoareaderquerywithitsfixedunique
ID.ThisfixedIDenablestrackingoftagsandthebearers.
Sometagscarryinformationabouttheobjectstheyare
attachedto[9].Areaderdetectsthetagsthatisattached
toorembeddedintheselecteditems.Itvariesinsize,
weightandmaybestationaryormobile.Thereader
communicateswiththetagthroughthereaderantenna,
whichbroadcastsradiowavesandreceivesthetag’s
responsesignalswithinitsreadingarea.Afterthesignals
fromthetagaredetected,thereaderdecodesthemand
passestheinformationtomiddleware[6].
Typically,anRFIDmiddlewareplatformperforms
aggregationofdataacrossdifferentreadersand
forwardingofrelevantdatatosubscriberserversor
application‐levelsystems,andpersistentstoragefor
receiveddata[10].However,anRFIDmiddlewareis
oftengiventhetaskofmanaging,monitoringand
configuringthedifferentreaders[11].
InVTCE,anAlienALR‐9800EnterpriseRFIDReader
typeischosen,becauseitfulfilsmostoftheRFID
hardwareʹspropertyrequirements,suchasitoperates
withUHFfrequency,multi‐staticantenna,multiple
antennaportsandsupportEthernetconnection.
3.VTCESystemArchitecture
TheVTCEprojectisdividedintotwoparts,softwareand
hardware.Thesoftwarepartisprogrammedusingthe
MicrosoftVisualBasic2010programandthelarge
databasesystemisdesignedbyMicrosoftSQLServer
2008R2ManagementStudio.Thehardwarepartconsists
ofRFIDreaders,whicharesimulatedbytheuseofthe
RifidiPlatform[12]andRoadsandTrafficIntersections
Simulator(RTIS).Rifidiisaprogramdesignedto
simulateanRFIDsystemandtoperformasahardware
reader.ItisthepremieropensourcesimulatorforRFID.
Also,wedesignedanRTISprogramtosimulatethe
physicalmovementsofvehicles,theRFIDtagson
vehiclesandtheroadnetworkwithtrafficintersections.
Figure2.ArchitectureofVTCEbasedonRFID
2Int. j. eng. bus. manag., 2012, Vol. 4, 30:2012 www.intechopen.com
ThesystemarchitectureoftheVTCEisshowninFig.2.It
canbeseenthatthissystemmainlyconsistsofRFIDtags
attachedtoallvehicles,twoRFIDreadersandfour
antennasoneachreader.IneachbranchtwoRFID
antennasareinstalled,theycansimultaneouslyscanin
oppositedirectionsfromthetwovehiclesandcanrecord
relevantinformationforeachvehicle.
ThetwoRFIDantennasarelocatedinthemedianisland
oftheroadnearthetrafficintersectionandseparatedbya
convenientdistance.Thisarchitectureofarrangingthe
directionofantennasmeanstheantennas’RFradiation
areasdonotoverlapwitheachother.IntheCCS,
accordingtotheorderofreceivingthesametagIDfrom
twodifferentantennas,thedirectionofvehiclemovement
willbeknownfromitsentrytoitsexit.Inthissystem
architecture,theVTCEwillmonitorthepathdirectionfor
allvehiclesinthetrafficintersectionsinreal‐time.
TheCCSperformsmanagement,monitoringand
maintenanceonthecommunicationwiththereaders,and
containsthemiddlewareanddatabaseserver[10].Figure
3showshowtheCCSisconnectedwiththeRFIDreaders
andthedatabase.
Figure3.ThelayoutoftheVTCEenvironment
4.VTCESoftware
TheVTCEsoftwareisrundirectlyontheCCS,the
middlewarerequeststhedatabasetoobtainthe
requirementstoestablishconnections.These
requirementsincludethetrafficintersection’sID,the
RFIDreader’sIPandPortandtheRFIDreaderʹs
UsernameandPassword.Aftertheresponseisreceived
fromthedatabasethesystemstartstheconnectionwith
thereadersandbeginsrequestingthereaderstogetthe
gatheredtaglistoverperiodsofonesecond.
Thedatareceivedintheformofapackagefromthe
readermustbeprocessedthroughseveralstepsand
filters,asshowninFig.4.Thepackageinformationis
filteredastagID,antennanumber,dateandtime.This
informationwillbestoredinanonlinedatatable.Ifthe
antennanumberiseven,thepackagedataarestoredina
temporarytableandiftheantennanumberisodd,the
systemrequeststosearchinthetemporarytableforthat
tagID.Whenbothevenandoddpackagesareobtained
together,thesystemwillinferthevehicleIDthathas
passedthrough,trafficintersectionID,from‐road,to‐
road,date,in‐timeandout‐time.Theinferredinformation
willbestoredinthevehiclelocationtable.Thenextstep
istocheckwhetherthisvehicleIDisinablacklisttable
ornot.Ifthisisdetectedthenthesystemdisplaysa
warningmessagethatincludestheIDofthevehicle
passingthroughthetrafficintersection,from‐road,to‐
road,dateandthetime.
Figure4.Flowchartofmiddlewareperformance
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Fawzi M. Al-Naima, and Hassan A. Hamd:
Vehicle Traffic Congestion Estimation Based on RFID
www.intechopen.com
5.RoadsandTrafficIntersectionsSimulator(RTIS)
5.1TheRTISArchitecture
BeforeintroducingtheVTCEprojectintoareal‐life
situation,theability,functionality,efficiencyandfurther
effectshavetobetestedcarefully.Toevaluatethe
improvementsthatcanbeachieved,simulationshaveto
beconducted.Hence,arealisticsimulationofroadand
trafficintersectionscenariosisneeded.Various
parametersareneededtosimulatethetraffic,theVTCE
communicationwithreaders,theapplicationandthe
environment[2].
Trafficincludesthephysicalmovementsofvehiclesonan
arbitraryroadnetwork.WiMAXwirelessEthernet
communicationbetweenCCSandreaders[13]is
simulatedviaWi‐FiwirelessEthernetcommunication.
Applicationsimulationmeansthesimulationof
applicationsthataretobeintegratedinreal‐world
vehicles.Forthispurpose,innervehicleinterfaceshaveto
beemulatedtoallowtheapplicationtointeractwith
RFIDreaders,suchasattachedRFIDtagsonvehicles.The
lastpartistheenvironmentsimulationwhichincludes
theroadnetworkwithtrafficintersections.Also,aTCP/IP
serverisbuilttosimulatetheconnectionmethodinRTIS
suchastheAlienALR‐9800EnterpriseRFIDreader.The
VTCEconnectionwithRTISisliketheconnectionwith
therealAlienreaders.
5.2TheRTISScenario
ToevaluatetheeffectivenessoftheVTCEandtoidentify
potentialproblems,asimplesimulationscenariohastobe
considered.Forthisreason,aspecialregionisselected
whichhassevenin‐outwaysandfivetrafficintersections.
Oneintersectioniscomposedofthreeroadsintersected
andtheothersarecomposedoffourroadsintersected.
Thisregionischosenbecauseitprovidesagoodroad
structureforVTCEtests.
AnintersectionIDisgiventoeachintersectionsuchas
(146,147,…,150).TenRFIDreadersaretobesimulated;
tworeaderstoeachintersectionasshowninFig.5.
Severalvehiclesaresimulatedandeachvehicleisgivena
specificvehicleIDandtagID.Thespeedofvehicles
movingcanbecontrolledinRTIS.Thesevehiclesmove
ontheroadnetworkalongarandompath.Severalroutes
aredesignedforeachvehicleonroadnetwork.The
vehicleselectsaspecificroutetopassthroughby
implementingarandomfunction.
TheRTISsimulatestheRFIDreadertoscanandreceivetag
IDsfromvehicles.Thevirtualreaderaccumulatesthese
datauntilitreceivesthe‘GetTagList’instructionfromthe
VTCE.Then,itwillsendthegathereddatatoVTCEinCCS
bytheTCP/IPserver.TheRTISisdesignedforcreatinga
scenariothatisalmostreal.Alltheunnecessaryor
unpredictablefactorsthatcaninfluencetheresults,suchas
sideroadtrafficorcomplextrafficlightsystems,are
avoidedinordertoprovidesignificantresults.
Figure5.ThevehicleandroadnetworkinRTIS
6.ProposedTrafficCongestionEstimationStrategy(TCES)
Reportingroadtrafficcongestioncanbeaconfusingtask
sincethereisnoadoptedstandardformeasuring
congestion[14].Typicalusersneedaconciseandeasyto
understandtrafficreport.Thenormaltrafficsituationcan
beroughlycategorizedintotwostates,openand
congested[15],butsuchaclassificationisnotenoughto
describethetrafficsituation.Thus,intheVTCEproject
threetrafficpatternshavebeenadoptedtofacilitatequick
andeasystepstounderstandthereport[14],namely,Red
(TrafficJam),Yellow(SlowMoving),andGreen(Free
Flow)aredefinedasthefollowing:
TrafficJam:thereareaverylargenumberof
vehiclesandalmostallofthemrunveryslowlyand
thiswillberepresentedbyredcolour.
SlowMoving:therearealargenumberofvehicles
andmostofthevehiclesrunathalfspeedandthis
willberepresentedbyyellowcolour.
FreeFlow:thereareaminimumnumberofvehicles
andthevehiclesrunatnormalspeedintheregion
ofinterestandthiswillberepresentedbygreen
colour.
Todetermineacongestionlevel,thefollowingthreesteps
arecarriedout:
6.1ComputingtheAverageTimeSpent
Tocomputetheaveragetimethatisrequiredtopassthe
street,inthefirststepthesystemgetsthetimefromGUI.
Then,thesystemgoesbackfiveminutes.Therewith,it
requeststheIDsofthevehiclesthathaveleftthestreet
withinthoselastfiveminutes.Inthenextstep,thesystem
recallstheentrytimeintothestreetforthosevehicles.
Then,itsubtractstheexittimefromentrytimeforeach
4Int. j. eng. bus. manag., 2012, Vol. 4, 30:2012 www.intechopen.com
vehicleasdepictedinFig.6.Thelaststepiscomputing
thetimeaverageusingthesumofthespenttimeofallthe
vehiclesanddividesitbythenumberofvehicles.
6.2ComputingtheAverageSpeedofVehicles
Thesystemwillcomputetheaveragespeedofvehicles
afteritgetsthedistanceofthestreetfromthetraffic
intersectionstable.Thesystemcomputestheaverage
speedofvehiclesbydividingthedistanceonaverage
timespent.
6.3DeterminingtheCongestionLevel
Afterthesystemobtainstheaveragespeedofvehiclesin
thestreet,inthenextstep,congestionlevelsareclassified
usingspeedintothreelevels:red,yellowandgreen.The
VTCEprojectusestwoclassificationthresholds,γ and δ,
foradjustingparametersofthealgorithm,asfollows[14]:
Greenlevel,iftheaveragespeedislargerthanor
equaltoγ.
Yellowlevel,iftheaveragespeedlessthanγand
largerthanδ.
Redlevel,iftheaveragespeedislessthanδ.
Attheend,theuserobtainsfromthesystemtheaverage
requiredtimetakentopassthestreetandtheaverage
speedofvehiclesinthelastfiveminutes,aswellas
estimatingthetrafficcongestionlevel.
Figure6.Theflowchartfortrafficcongestionstatusestimation
7.VTCEApplications
7.1TheTrafficCongestionAppraisal
TheVTCEsystemsupportssomeapplicationsforthe
datathataregatheredandanalysed.Thetraffic
congestionappraisalisoneofthem.Thisapplicationcan
estimatethecongestionintwoconditions;alongthestreet
orwithintheintersection.
Figure7showstheGUIofintersectioncongestion
appraisal.Appraisaloftrafficintersectioncongestionis
usedtoestimatetherequiredtimetocrosstheintersection.
Itcalculatestheaverageoftherequiredtimetocrossthe
distancebetweentheantennaofentryandtheantennaof
exit.ThisapplicationusesonlythefirststepoftheTCES.
ThetrafficintersectionIDshouldbeenteredinitsspecific
field.Thecongestioncanbeappraisedforthetimebeing
(default)oratthepreviousdateandtimebyselectingits
buttonthenspecifyingthedateandtime.Thesuggested
systemuses α andβcriteriatoestimatethestatusof
congestionintheintersection.Thesystemenablesαandβ
tobechangedbytheuserviaclickingontheSettingbutton
thatactivatesitstextsboxes. α and β arethetimerange
thatarerequired(inseconds)tocrosstheintersection.By
clickingontheCongestionStatusbutton,thesystemwill
computeanddisplayresultsasthenumberofvehicles
passedthroughtheintersectioninthelastfiveminutes,the
averageoftherequiredtimetopassthisintersection(in
seconds)andtheintersectioncongestionstatusasFree
Flow(green),SlowMoving(yellow)orTrafficJam(red).
Figure7.Thetrafficintersectionscongestionappraisal
Figure8showstheGUIofstreettrafficcongestion
appraisal.Thestreetcongestionappraisalisavery
importantapplicationofVTCE.Usingthisapplicationthe
streetcongestionstatuscanbeestimatedinreal‐time.The
usermustspecifythestreetandthedirectionoftraffic.
ThisisdonebygivingtheIDoftheintersection,i.e.,
from‐intersectionto‐intersection.Thetimemustbe
specifiedatthepresenttime(default)orontheprevious
dateandtimebyselectingitsbutton.Inthisapplication
thesystemuses γ and δ criteriatodecidethestatusof
5
Fawzi M. Al-Naima, and Hassan A. Hamd:
Vehicle Traffic Congestion Estimation Based on RFID
www.intechopen.com
trafficcongestioninthestreet.γand δ aretherangeof
vehicles’speedcriteria(inkm/Hr)thatpassalongthe
street.VTCEsystemenablestheusertochangeγandδ.
Figure8.Thestreettrafficcongestionappraisal
ThisapplicationusesallstepsintheTCES.Byclickingon
theCongestionStatusbutton,thesystemwilldisplaythe
requiredtimeaveragetopassthatstreet,theaverage
speedofvehiclesthatpassedalongthestreetandthe
streettrafficcongestionstatus.
7.2TrafficCongestionStatusWebsite
TheVTCEsystemcollectsusefuldatafromintersections
aboutvehiclemovements.Thesedataareonlyallowed
foradministratoruse.Twowebsitesaredesignedtoallow
anyusertobenefitfromtrafficcongestionestimation
applicationsasshowninFig.3.Viathesewebsites,the
usercanavoidtrafficcongestionbycheckingthe
congestionstatusbyintersectionorstreet.
Theintersectioncongestionstatusestimationwebsite
estimatestherequiredtimetocrosstheintersection.This
applicationusesonlythefirststepoftheTCES.Theclient
(user)shouldenterthetrafficintersectionID.Thewebsite
loadsthepresentdateandtime,and α and β criteria,
automatically.Thewebsiteenablestheusertochangeαand
βcriteriaandspecifypreviousdateandtime.Bypressingon
theIntersectionCongestionStatusbuttonthewebsite
computesanddisplaystheresultsonline,seeFig.9.
Thestreetcongestionstatuswebsiteisdesignedforany
user(driver)tocheckthecongestionstatusofastreet
beforejoiningit.Theuseroughttodefinethestreetby
from‐intersectionIDto‐intersectionID.Thewebsiteloads
thepresentdateandtime,andγand δ criteria,
automatically,ortheycanbedefinedmanually.This
websiteusesallstepsintheTCES.ByclickingontheStreet
CongestionStatusbutton,thesystemwilldisplaythe
averageoftherequiredtimetopassthatstreet,theaverage
speedofvehiclesthatpassedalongthestreetandthestreet
trafficcongestionstatus.Fig.10showstheInternetpage.
Figure9.Thewebsiteofintersectioncongestionestimation
Figure10.Thewebsiteforstreetcongestionestimation
7.3StreetTrafficCongestionAppraisal/SMSServer
IntheVTCEproject,theStreetTrafficCongestionAppraisal
/SMSServer(STCA/SMS)isdesignedsothatausercan
obtaininformationaboutstreetcongestionwithoutaccessto
theInternet.ThisisdonebysendinganSMSmessagetothe
STCA/SMSserver.TheSTCA/SMSserveriscomposedof
twoparts:STCAandGSMmodem,(seeFig.2).TheSTCA
receivestherequestfromtheGSMmodemandcalculates
thecongestionstatusatthepresenttimebytheuseofTCES,
seeFig.11.Then,itsendsthereplytotheGSMmodem.
AGSMmodemisawirelessmodemthatconnectsa
computertoaGSMnetwork.LikeaGSMmobilephone,a
GSMmodemrequiresaSIMcardinordertooperate.Figure
12showstheestablishedcommunicationbetweenthe
cellularnetworkandthecomputerviaGSMModem[16].
6Int. j. eng. bus. manag., 2012, Vol. 4, 30:2012 www.intechopen.com
Figure11.StreetTrafficCongestionAppraisal/SMSServer
Figure12.GSMmodemcommunications
AnexternalGSMmodemisconnectedtoacomputerbya
serialcable.Itispossibletomakeandreceivephonecalls
andsendtextmessages.ATcommandsmustbeusedfor
establishingcommunicationbetweentheGSMmodemand
thecomputer[16].ATcommandsarethesetofcommands
thatarespecifiedforcontrollingaGSMphoneormodem
andmanagingtheSMSfeatureofGSM.IntheVTCE
project,theSonyEricssonMobilePhoneModemAAD‐
3880020‐BVhasbeenusedalongwiththeSonyEricsson
built‐inmodemsoftware.TheVTCEmodemis
programmedbyATcommandswithProtocolDescription
Unit(PDU)formatmode[17].Itisusedtosend/receive
SMSmessagesto/fromtheuser(thedriver).TheGSM
modemreceivestheSMSmessagefromGSMnetworkand
sendsittoSTCAandviceversa,asdepictedinFig.13.
Figure13.SonyEricssonGSMmodemconfigurations
Theusermustsendamessageinaspecificformat.The
messageoughttostartwiththe(STCA)symbol,capitalor
small,followedbyaspace.Then,theusermustspecify
thestreetbyenteringtheIDoftheintersectionfollowed
byaspaceandtheIDofthenextintersection,forexample
(STCA150149).Themessageissenttotheservice
number;themodemreceivesamessageandsendsitto
STCA/SMSserver.Theserverwillsendthereplytothe
modemtosendittotheuser.
8.Conclusion
VehicleTrafficCongestionEstimationbasedonRFIDisa
projectwithagoaltogatherinformationaboutthe
vehiclespassinginanyspecifictrafficintersection.This
informationisthenutilizedtodeterminethetrafficstatus
ofaroadnetwork.ThisgoalisrealizedbyinstallingRFID
readersinalltheseintersectionsandattachingRFIDtags
toallvehicles,whereuponthesereaderssendthe
informationtoaCCS.TheproposedVTCEprojectuses
theTCEStoclassifythereportintothreetrafficpatterns,
namely,Red(TrafficJam),Yellow(SlowMoving)and
Green(FreeFlow),whichmayalsobeextendedto
classifyfivepatterns,ifneeded.TheproposedVTCE
projectusesthisinformationinthreedifferent
applications;TheTrafficCongestionAppraisalforthe
administrator,TrafficCongestionStatusWebsitefor
InternetusersandStreetTrafficCongestionAppraisal/
SMSServerforGSMmobileusers.Itisimportanttonote
thatiftheproposedVTCEsystemistobeimplemented,
thenlargedataneedtobeprovidedbythetraffic
authoritiesfortestingtoensuretherobustnessofthe
system.
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... Nevertheless, the standard Q algorithm is not designed to support the RFID system with different delay requirements. Example of RFID system with different delay requirements is Automatic Vehicle Identification (AVI) system [9][10][11][12][13][14]. In the Automatic Vehicle Identification system, the tags that are attached to the emergency vehicles such as ambulances and fire trucks [15][16][17][18][19] needed to be identified before the other vehicles. ...
Performance Evaluation of Anti-Collision Algorithms for RFID System with Different Delay Requirements
Article
Full-text available
  • Jan 2017
The main purpose of Radio-frequency identification (RFID) implementation is to keep track of the tagged items. The basic components of an RFID system include tags and readers. Tags communicate with the reader through a shared wireless channel. Tag collision problem occurs when more than one tag attempts to communicate with the reader simultaneously. Therefore, the second-generation UHF Electronic Product Code (EPC Gen 2) standard uses Q algorithm to deal with the collision problem. In this paper, we introduce three new anti-collision algorithms to handle multiple priority classes of tags, namely, ????, ???? and ?????? algorithms. The goal is to achieve high system performance and enable each priority class to meet its delay requirement. The simulation results reveal that ?????? algorithm is more effective than the ???? and ???? algorithms as it is designed to flexibly control and adjust system parameters to obtain the desired delay differentiation level. Finally, it can conclude that the proposed ?????? algorithm can control the delay differentiation level and yet maintain high system performance.
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... First RFID-based tolling system appeared in the USA in 1991 [10]. RFID usages in entrance control systems, tolling and others are devoted a significant amount of researches [2,5,[7][8][9]21]. RFID can be applied to solve other problems, such as performance enhancement of the traffic law enforcement systems making use of cameras to identify vehicle licence plates [19,20]. ...
Analysis and Simulation of UHF RFID Vehicle Identification System
Conference Paper
  • Nov 2016
In this paper a model of UHF RFID Vehicle Identification System based on EPC Class1 Gen2 is described. The model takes into account the influence of protocol settings, antenna and tranceiver parameters and signal propagation along roads on the system performance. It is shown that the two-ray pathloss model and Rayleigh distribution for BER computation allows to simulate RFID system operation adequately. The estimated protocol settings providing reliable vehicle identification at speed up to 220 kmph are given.
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Overview of Research Works on Applications of UHF RFID on Vehicles for Data Transmission
Chapter
  • Mar 2024
  • Lect Notes Comput Sci
RFID (Radio Frequency Identification) technology has found wide application in many areas of science and technology and people’s everyday lives (in libraries, shops, when registering passengers on the subway and buses, etc.). Every year the number of RFID applications is growing, and at the same time the number of scientific papers exploring certain issues related to this topic is increasing. One of these rapidly developing areas is the use of RFID to identify fast-moving transport objects (cars, trains), which is currently poorly reflected in existing reviews. This article fills this gap. The paper provides an overview of publications in the field of RFID technologies and standards. A description of theoretical and experimental results is given, as well as the architecture and hardware and software for the practical implementation of land vehicle identification systems used in various fields.
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AllSpark: Enabling Long-Range Backscatter for Vehicle-to-Infrastructure Communication
Article
  • Dec 2022
Long-range backscatter communication has the potential to provide enough time and space for vehicles to detect traffic information, which is an attractive solution for vehicle-toinfrastructure (V2I) communication. However, existing backscatter studies either require the tag to be close to the carrier source (ambient backscatter) or have poor receiver sensitivity (RFID), making it challenging to satisfy the high range requirements of V2I communication. In this paper, we develop AllSpark to investigate the feasibility of long-range communication enabling backscatter to be applied to V2I. Specifically, we first redesign the tags radio frequency (RF) front-end to amplify the incident signals. We design high-gain directional antennas for the tag and reader to increase RSS to compensate for the enormous dualpath loss in backscatter systems. Second, we present EC-Net, an end-to-end demodulator that selectively enhances signal features (denoising) and is tuned to lower classification (demodulation) error to maximize demodulation accuracy. Furthermore, we adopt convolutional encoding for tag data and use the output probability of EC-Net to design an effective soft decoder to resist occasional interference and noise, improving communication robustness. Our prototype and experiments outdoors verify the effectiveness of AllSpark which can provide a communication range of 700 m. Even in a moving scene, it can achieve a communication range of 600 m, demonstrating that AllSpark has the potential to be applied for autonomous driving and low-flying drones to detect traffic conditions.
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Estimation of RFID Tag's direction based on LANDMARC algorithm
Article
  • Jan 2018
The objects monitoring is a very important research and to effectively monitor objectives of interest it's necessary to be aware of the object's movement direction in a monitored area. This study presents an accurate method to determine the direction of the tagged object through a Radio Frequency Identification (RFID)-based system, using LANDMARC algorithm. A location sensing prototype system that uses (RFID) technology for locating objects. In this research method use Received Signal Strength Indicator (RSSI) technique to estimate the direction of the object. This method considers as an effective tag movement direction detection in which an original tag communication system is used as much as possible without using optional equipment, also, this algorithm able to reduces the number of antennas used to estimate the direction of the object to a lower's number as possible. The simulation results prove the accuracy of this method with a low complexity.
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UHF RFID in Automatic Vehicle Identification: Analysis and Simulation
Article
  • Sep 2017
In this paper the application of UHF RFID in automatic vehicles identification is studied by the means of a new simulation framework. The system under study consists of the stationary readers with antennas mounted over the road lanes and vehicles license plates equipped with passive EPC Class 1 Generation 2 UHF RFID tags. The simulation includes protocol settings, antennas parameters and propagation media conditions. Propagation analysis considers Doppler shift to compute the path loss of moving tags properly. The estimations of an inventory round duration, the number of rounds the tag participates in and the tag identification probability are provided. The paper presents vehicles identification probabilities computed for different protocol settings and movement speeds. In particular, it was shown that a vehicle could be identified with 0.9 probability by EPC values only when the vehicle speed is 120 km/h or even more. If the vehicle is identified with both EPC and TID banks values, the same 0.9 probability is achievable when the vehicle speed is under 90 km/h. The results of the simulation coincide with the experiment results involving the city buses.
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An Application of RFID Localization in Lane Recognition for Vehicles
Chapter
  • Jan 2015
Due to the surge in the number of vehicles throughout the world, traffic problems become increasingly severe. Thus, the implementation of lane recognition plays an important role in Intelligent Transportation Systems (ITS). In this paper, we present a RFID based positioning approach for lane recognition which is also practical for scenarios like tunnels or multilayer viaduct where GPS is not available now. As Phase Difference of Arrival is the essential information for localization, two UHF RFID phase extraction schemes have been discussed. Experimental results show that both of the two schemes can achieve an error recognition rate under 6.0 %. The sub-sampling scheme has a better performance than Zero-IF Receiver at the expense of system complexity.
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RFID Emulation in Rifidi Environment
Article
Full-text available
  • Jan 2009
RFID is a technology for radio frequency identification that enables software systems to automatically detect and identify objects in the real world. Thanks to this ability, RFID is of interest for numerous companies; unfortunately, the evaluation of a new business scenario involving RFID demands significant in-vestments in time, hardware, and infrastructure. Instead, Rifidi is a tool that quickly and realistically emulate RFID scenarios in order to explore their possibil-ities before investing in them. We present here an analysis of Rifidi, showing how to practically exploit it to design a new business process, and discuss pros and cons in its use. Furthermore, as a case study, we consider a new application that we have devised and that could be of interest for companies: an RFID-based, vir-tual shop assistant.
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Estimating Road Traffic Congestion using Vehicle Velocity
Conference Paper
Full-text available
  • Jul 2006
This study investigates an alternative way to estimate degrees of road traffic congestion based on routine GPS measurements from main roads in urban areas of Bangkok, Thailand. We classify three levels of traffic congestion according to the weighted exponential moving averages of measured GPS speed. Human perception is used to obtain classification thresholds and evaluated the performance of the proposed method. The benefits of our proposed method over existing techniques is that it is simple, easy to understand, and compatible with existing traffic report systems in Bangkok
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An RFID electronic tag based automatic vehicle identification system for traffic iot applications
Article
  • May 2011
This paper introduces the design and implementation of an active RFID tag based system for automatically identifying running vehicles on roads and collecting their data. The design principles and the archi- tecture of the system are presented, including active electronic tags and reading equipments (readers and antennas), the monitoring base station deployment, the two-layered network construction, and the monitoring software. To solve identification uncertainty problems, the two key techniques are: anti- interference protocol and data clearing algorithm are proposed. Finally, the effectiveness and efficiency of the system is analyzed. The system will have wide applications in traffic IOT (Internet of Things) to support traffic monitoring, traffic flow statistics, traffic scheduling, and special vehicle tracking.
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Automatic vehicle identification (AVI) system based on RFID
Article
  • Jul 2010
RFID is a Radio Frequency Identification technology, which has a unique advantage in data collection and data transmission. Based on the expatiate of the identification principle, the system structure, and the characteristics of the RFID, this paper introduces the structure, technical target and application of the AVI system Based on the RFID.
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RFID Explained: A Primer on Radio Frequency Identification Technologies
Article
  • Jan 2006
This lecture provides an introduction to Radio Frequency Identification (RFID), a technology enabling automatic identification of objects at a distance without requiring line-of-sight. Electronic tagging can be divided into technologies that have a power source (active tags), and those that are powered by the tag interrogation signal (passive tags); the focus here is on passive tags. An overview of the principles of the technology divides passive tags into devices that use either near field or far field coupling to communicate with a tag reader. The strengths and weaknesses of the approaches are considered, along with the standards that have been put in place by ISO and EPCGlobal to promote interoperability and the ubiquitous adoption of the technology. A section of the lecture has been dedicated to the principles of reading co-located tags, as this represents a significant challenge for a technology that may one day be able to automatically identify all of the items in your shopping cart in a just few seconds. In fact, RFID applications are already quite extensive and this lecture classifies the primary uses. Some variants of modern RFID can also be integrated with sensors enabling the technology to be extended to measure parameters in the local environment, such as temperature & pressure. The uses and applications of RFID sensors are further described and classified. Later we examine important lessons surrounding the deployment of RFID for the Wal-Mart and the Metro AG store experiences, along with deployments in some more exploratory settings. Extensions of RFID that make use of read/write memory integrated with the tag are also discussed, in particular looking at novel near term opportunities. Privacy and social implications surrounding the use of RFID inspire recurring debates whenever there is discussion of large scale deployment; we examine the pros and cons of the issues and approaches for mitigating the problems. Finally, the remaining challenges of RFID are considered and we look to the future possibilities for the technology.
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RFID Design Principles
Article
  • Jan 2008
This cutting-edge book serves as a comprehensive introduction to RFID, offering you a detailed understanding of design essentials and applications, and providing a thorough overview of management issues. By comparing RFID with WLAN and Bluetooth, this practical resource shows you how RFID technology can help you overcome many design challenges and limitations in the field. The book explains the design of electronic circuits, antennas, interfaces, data encoding schemes, and complete RFID systems. Starting with the basics of RF and microwave propagation, you learn about major system components including tags and readers. This hands-on reference distills the latest RFID standards, and examines RFID at work in supply chain management, intelligent buildings, intelligent transportation systems, and tracking animals. RFID is controversial among privacy and consumer advocates, and this book looks at every angle concerning security, ethics, and protecting consumer data. From design details... to applications... to socio-cultural implications, this authoritative volume offers the knowledge you need to create an optimal RFID system and maximize its performance.
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Implementation of National Traffic Information Collection Systems in Ubiquitous Environments
Conference Paper
  • Jan 2009
The objectives of the research include to develop a novel Traffic Information Collection System (TICS) based on RFID, and to create a chance to lead ubiquitous environments in the transportation area.
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Optimization Management System for Level Crossing Traffic Based on RFID
Conference Paper
  • May 2010
This paper describes the use of radio frequency identification technology and computer technology to solve level crossing problems in the management of traditional vehicles, proposed management system optimized design, including the hardware architecture and system software; built automatic vehicle identification and information management platform; for Intelligent Transportation Systems laid an important foundation; for the Smooth Traffic Project of urban road has an important practical value.
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Radio frequency identification (RFID)
Article
  • Feb 2006
  • COMPUT SECUR
First conceived in 1948, Radio Frequency Identification (RFID) has taken many years for the technology to mature to the point where it is sufficiently affordable and reliable for widespread use. From Electronic Article Surveillance (EAS) for article (mainly clothing) security to more sophisticated uses, RFID is seen by some as the inevitable replacement for bar codes. With increasing use comes increasing concern on privacy and security. Clearly there is considerable work to be undertaken before RFID becomes as pervasive as bar codes although the tempo of change is increasing rapidly.
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