电气专业毕业论文英文文献及翻译附原文基于GPRS的智能交通系统.docx
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电气专业毕业论文英文文献及翻译附原文基于GPRS的智能交通系统.docx
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电气专业毕业论文英文文献及翻译附原文基于GPRS的智能交通系统
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外文原文
TrafficAssignmentForecastModelResearchinITS
Introduction
Theintelligenttransportationsystem(ITS)developsrapidlyalongwiththecitysustainabledevelopment,thedigitalcityconstructionandthedevelopmentoftransportation.OneofthemainfunctionsoftheITSistoimprovetransportationenvironmentandalleviatethetransportationjam,themosteffectivemethodtogaintheaimistoforecastthetrafficvolumeofthelocalnetworkandtheimportantnodesexactlywithGISfunctionofpathanalysisandcorrelationmathematicmethods,andthiswillleadabetterplanningofthetrafficnetwork.Trafficassignmentforecastisanimportantphaseoftrafficvolumeforecast.Itwillassigntheforecastedtraffictoeverywayinthetrafficsector.Ifthetrafficvolumeofcertainroadistoobig,whichwouldbringontrafficjam,plannersmustconsidertheadoptionofnewroadsorimprovingexistingroadstoalleviatethetrafficcongestionsituation.Thisstudyattemptstopresentanimprovedtrafficassignmentforecastmodel,MPCC,basedonanalyzingtheadvantagesanddisadvantagesofclassictrafficassignmentforecastmodels,andtestthevalidityoftheimprovedmodelinpractice.
1Analysisofclassicmodels
1.1Shortcuttrafficassignment
Shortcuttrafficassignmentisastatictrafficassignmentmethod.Inthismethod,thetrafficloadimpactinthevehicles’travelisnotconsidered,andthetrafficimpedance(traveltime)isaconstant.Thetrafficvolumeofeveryorigination-destinationcouplewillbeassignedtotheshortcutbetweentheoriginationanddestination,whilethetrafficvolumeofotherroadsinthissectorisnull.Thisassignmentmethodhastheadvantageofsimplecalculation;however,unevendistributionofthetrafficvolumeisitsobviousshortcoming.Usingthisassignmentmethod,theassignmenttrafficvolumewillbeconcentratedontheshortcut,whichisobviouslynotrealistic.However,shortcuttrafficassignmentisthebasisofalltheothertrafficassignmentmethods.
1.2Multi-waysprobabilityassignment
Inreality,travelersalwayswanttochoosetheshortcuttothedestination,whichiscalledtheshortcutfactor;however,asthecomplexityofthetrafficnetwork,thepathchosenmaynotnecessarilybetheshortcut,whichiscalledtherandomfactor.Althougheverytravelerhopestofollowtheshortcut,therearesomewhosechoiceisnottheshortcutinfact.Theshorterthepathis,thegreatertheprobabilityofbeingchosenis;thelongerthepathis,thesmallertheprobabilityofbeingchosenis.Therefore,themulti-waysprobabilityassignmentmodelisguidedbytheLOGITmodel:
(1)
Where
istheprobabilityofthepathsectioni;
isthetraveltimeofthepathsectioni;θisthetransportdecisionparameter,whichiscalculatedbythefollowprinciple:
firstly,calculatethe
withdifferentθ(from0to1),thenfindtheθwhichmakes
themostproximatetotheactual
.
Theshortcutfactorandtherandomfactorisconsideredinmulti-waysprobabilityassignment,therefore,theassignmentresultismorereasonable,buttherelationshipbetweentrafficimpedanceandtrafficloadandroadcapacityisnotconsideredinthismethod,whichleadstotheassignmentresultisimpreciseinmorecrowdedtrafficnetwork.Weattempttoimprovetheaccuracythroughintegratingtheseveralelementsaboveinonemodel-MPCC.
2Multi-waysprobabilityandcapacityconstraintmodel
2.1Rationalpathaggregate
Inordertomaketheimprovedmodelmorereasonableintheapplication,theconceptofrationalpathaggregatehasbeenproposed.Therationalpathaggregate,whichisthefoundationofMPCCmodel,constrainsthecalculationscope.Rationalpathaggregatereferstotheaggregateofpathsbetweenstartsandendsofthetrafficsector,definedbyinnernodesascertainedbythefollowingrules:
thedistancebetweenthenextinnernodeandthestartcannotbeshorterthanthedistancebetweenthecurrentoneandthestart;atthesametime,thedistancebetweenthenextinnernodeandtheendcannotbelongerthanthedistancebetweenthecurrentoneandtheend.Themulti-waysprobabilityassignmentmodelwillbeonlyusedintherationalpathaggregatetoassigntheforecasttrafficvolume,andthiswillgreatlyenhancetheapplicabilityofthismodel.
2.2Modelassumption
1)Trafficimpedanceisnotaconstant.Itisdecidedbythevehiclecharacteristicandthecurrenttrafficsituation.
2)Thetrafficimpedancewhichtravelersestimateisrandomandimprecise.
3)Everytravelerchoosesthepathfromrespectiverationalpathaggregate.
Basedontheassumptionsabove,wecanusetheMPCCmodeltoassignthetrafficvolumeinthesectoroforigination-destinationcouples.
2.3Calculationofpathtrafficimpedance
Actually,travelershavedifferentunderstandingtopathtrafficimpedance,butgenerally,thetravelcost,whichismainlymadeupofforecasttraveltime,travellengthandforecasttraveloutlay,isconsideredthetrafficimpedance.Eq.
(2)displaysthisrelationship.
(2)
Where
isthetrafficimpedanceofthepathsectiona;
istheforecasttraveltimeofthepathsectiona;
isthetravellengthofthepathsectiona;
istheforecasttraveloutlayofthepathsectiona;α,β,γaretheweightvalueofthatthreeelementswhichimpactthetrafficimpedance.Foracertainpathsection,therearedifferentα,βandγvaluefordifferentvehicles.Wecangettheweightedaverageofα,βandγofeachpathsectionfromthestatisticpercentofeachtypeofvehicleinthepathsection.
2.4ChosenprobabilityinMPCC
Actually,travelersalwayswanttofollowthebestpath(broadsenseshortcut),butbecauseoftheimpactofrandomfactor,travelersjustcanchoosethepathwhichisofthesmallesttrafficimpedancetheyestimatebythemselves.ItisthekeypointofMPCC.Accordingtotherandomutilitytheoryofeconomics,iftrafficimpedanceisconsideredasthenegativeutility,thechosenprobability
oforigination-destinationpointscouple(r,s)shouldfollowLOGITmodel:
(3)where
isthechosenprobabilityofthepathsection(r,s);
isthetrafficimpedanceofthepathsect-ion(r,s);
isthetrafficimpedanceofeachpathsectionintheforecasttrafficsector;breflectsthetravelers’cognitiontothetrafficimpedanceofpathsinthetrafficsector,whichhasreverseratiotoitsdeviation.Ifb→∞,thedeviationofunderstandingextentoftrafficimpedanceapproachesto0.Inthiscase,allthetravelerswillfollowthepathwhichisofthesmallesttrafficimpedance,whichequalstotheassignmentresultswithShortcutTrafficAssignment.Contrarily,ifb→0,travelers’understandingerrorapproachesinfinity.Inthiscase,thepathstravelerschoosearescattered.ThereisanobjectionthatbisofdimensioninEq.(3).Becausethedeviationofbshouldbeknownbefore,itisdifficulttodeterminethevalueofb.Therefore,Eq.(3)isimprovedasfollows:
,
(4)
Where
istheaverageofthetrafficimpedanceofalltheas-signedpaths;bwhichisofnodimension,justhasrelationshiptotherationalpathaggregate,ratherthanthetrafficimpedance.Accordingtoactualobservation,therangeofbwhichisanexperiencevalueisgenerallybetween3.00to4.00.Forthemorecrowdedcityinternalroads,bisnormallybetween3.00and3.50.
2.5FlowofMPCC
MPCCmodelcombinestheideaofmulti-waysprobabilityassignmentanditerativecapacityconstrainttrafficassignment.
Firstly,wecangetthegeometricinformationoftheroadnetworkandODtrafficvolumefromrelateddata.ThenwedeterminetherationalpathaggregatewiththemethodwhichisexplainedinSection2.1.
Secondly,wecancalculatethetrafficimpedanceofeachpathsectionwithEq.
(2),whichisexpatiatedinSection2.3.
Thirdly,onthefoundationofthetrafficimpedanceofeachpathsection,wecancalculatetherespectiveforecasttrafficvolumeofeverypathsectionwithimprovedLOGITmodel(Eq.(4))inSection2.4,whichisthekeypointofMPCC.
Fourthly,throughthecalculationprocessabove,wecangetthechosenprobabilityandforecasttrafficvolumeofeachpathsection,butitisnottheend.Wemustrecalculatethetrafficimpedanceagaininthenewtrafficvolumesituation.AsisshowninFig.1,becauseoftheconsiderationoftherelationshipbetweentrafficimpedanceandtrafficload,thetrafficimpedanceandforecastassignmenttrafficvolumeofeverypathwillbecontinuallyamended.Usingtherelationshipmodelbetweenaveragespeedandtrafficvolume,wecancalculatethetraveltimeandthetrafficimpedanceofcertainpathsect-ionunderdifferenttrafficvolumesituation.Fortheroadswithdif
ferenttechnicallevels,therelationshipmodelsbetweenaveragespeedstotrafficvolumeareasfollows:
1)Highway:
(5)
2)Level1Roads:
(6)
3)Level2Roads:
(7)
4)Level3Roads:
(8)
5)Level4Roads:
(9)
WhereVistheaveragespeedofthepathsection;
isthetrafficvolumeofthepathsection.
Attheend,wecanrepeatassigningtrafficvolumeofpathsectionswiththemethodinpreviousstep,whichistheideaofiterativecapacityconstraintassignment,untilthetrafficvolumeofeverypathsectionisstable.
译文
智能交通交通量分配预测模型
介绍
随着城市的可持续化发展、数字化城市的建设以及交通运输业的发展,智能交通系统(ITS)的发展越来越快。
ITS的主要功能之一就是改善运输环境,缓和交通阻塞。
为了达到这个目的,其中最有效的方法就是运用GIS功能中的路径分析法和相关的相数学分析法预测出交通网络的交通量以及重要的交通节点,这将是一个更好的交通网络规划。
交通分配预测是交通量预测的一个重要阶段。
它将把预测流量分配到每一个交通部门的道路上。
如果某些道路交通量太大,会带来交通堵塞。
规划者必须考虑修建新道路或者改善现有道路以缓和交通堵塞的状况。
本研究试图提出一个改进过的交通分配预测模型,MPCC。
这个模型是在分析现有的典型的交通分配预测模型优缺点的基础上提出的,并在实践中测试了改进模型的有效性。
1经典模型分析
1.1快捷交通分配
快捷交通分配是一种静态交通分配方法。
在这个方法中,车辆出行的交通负荷的影响是不考虑的,并且交通阻抗(行程时间)是一个常数。
每一对从始发站到终点站间的交通量将会被平均地分配到始发站和终点站的快捷通道上,而此时其它道路上的交通量则为零。
这种分配方法的优点是计算简单,而其明
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