电气专业英文文献及翻译附原文基于GPRS的智能交通系统.docx

电气专业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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电气专业英文文献及翻译附原文基于GPRS的智能交通系统

外文资料翻译

外文出处：

Geo-spatialInformation

Science10（3）:

213-217

附件：

1.外文原文；2.外文资料翻译译文。

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.Fortheroadswithdifferenttechnicallevels,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