土地利用与交通需求 PPT课件版 L3 Trip Generation

TripGeneration

出行生成杨超土地利用与交通需求分析同济大学email:tongjiyc@2WhatisTransportation?TransportationinvolvesthemovementofpeopleandgoodsChangeinspace,time,and“utility”UtilityisameasureofsatisfactionorusefulnessTransportationisthereforegenerallynotanendinitself,butoccurstosatisfydemandforsomeactivitiesatthedestination.Transportationisa“derived”demand3tripgenerationtripdistributionmodesplittripassignmentTijmrTiTijTijmStep1.Decisiontotravel:forecastthenumberoftripsthatwillbemadeStep2.Choiceofdestination:determinewheretripswillgoStep3.Choiceoftravelmode:predicthowtripswillbedividedamongavailablemodesStep4.Choiceofroute:predicttheroutesthattripswilltake,resultingintrafficforecastsforthehighwaysystemInputs:LanduseandsocioeconomicprojectionsTransportationsystemArtificialfeedbackTraditionalTrip-basedModel:4-StepTravelDemandModelor{Flows&LevelofService}4出行产生4出行吸引5出行分布6方式划分7交通分配（路径选择）89ThreeZoneExample104-StepProcedureExampleTripGenerationTripDistributionModeChoiceRouteChoiceOi,Dj Tij Tijm TijmrTAZOi123T4766110223TAZDj123T45908822340Tij1123Dj1030545231832901946588Oi4766110223ModeT32mcarbuswalkTotal

2015540PathT32carrR1R2R3Total512320fromiTrafficAnalysisZonetripproductiontripattractiontoj我国目前城市交通规划技术规范解决：要不要建、什么时候建、建多大规模、建在什么地方等问题通过规划，预先知道建设项目实施后的效果，避免盲目建设11住房和城乡建设部2010年3月发布“城市综合交通体系规划编制办法”

城市综合交通体系规划是城市总体规划的重要组成部分城市综合交通体系规划应当与城市总体规划同步编制，相互反馈与协调不同层次交通规划的重点交通综合网络规划/整体交通规划交通专项规划/分区交通专项规划城市交通发展目标和水平城市交通方式和交通结构重大设施的选址和用地规模实施规划的重要技术经济对策城市交通各子系统的发展目标，具体规划方案局部地区交通特征分析，提出改善处理的方案大型单体建筑项目实施对周边交通系统的影响各种运输系统的布局，各种交通的衔接方式大型交通枢纽的分布与用地范围道路网络布局、道路等级和功能划分规划方案技术经济评估，提出分期建设建议局部地区交通改善规划/交通影响分析/交通设施规划设计交通发展战略规划侧重于城市交通发展方向的研究侧重于配合城市总体规划的研究侧重于具体实施侧重于各子系统的自身分析重要设施运行功能分析与方案设计12上海市域交通小区划分市域309个小区中心城263个小区上海市域交通小区划分进出口19处，其中高速公路3条随着上海1966规划的提出和落实、第四次综合交通调查的开展，交通模型重新划分小区：全市约4000个小区，中心城约800个，郊区约2000个，其它为特殊点（机场、火车站、码头、地铁站等）和进出口每个小区面积（km2/个）中心城（约640km2)：1.25郊区（约5700km2)：2.8如何划分小区一般划分小区有几个层次：行政区域为大区、再根据街道、居委、村镇等划分中区、最后是小区根据自然条件和物理分割，结合行政边界确定小区边界，同时考虑现状及规划的高速公路、快速路作为边界小区的规模可参考类似城市，尽量保持小区出行量的接近水平注意特殊点、进出口的处理如何划分小区如何划分小区小区形心以小区形状的形心为基础，参考小区内用地的特征，将主要的交通产生、吸引点的地块作为形心位置小区连接线根据主要的交通出行方向确定大致方向（注意出行主流向），小区连接线一端为小区形心，另一端优先选择该方向的支路的出入口，尽量不连接到主干道小区连接线数量以4条为宜交通模型分区新趋势中观模型：杭州核心区14km2范围划分小区243个，平均小区面积约0.057km2小区用地分类参考城市规划规范、控制性详细规划以及类似城市（北京、深圳、上海）用地分类情况同时参考《北京市交通出行率指标》，确定用地分类原则用地分类备注住宅分为两类，即高档住宅，一般住宅工厂中观范围内工厂定为高新研发产业用地；外围区域分三类，即高新研发产业用地、一般工业用地、制造业工业用地商业文化娱乐办公与金融学校分三类：大专院校，中学，小学幼儿园医院旅馆分两类：三星级及以上旅馆，一般旅馆特殊吸引点商业中心，大型医院，政府办公用地（包括省、市、区三级），车站，机场，风景点，仓库货场，停车场，大型体育设施其他包括水域，绿地，市政公用设施总计九大类Step1:TripGeneration21TripGenerationAnalysis1ststepinthe4-steptravelforecastingprocessRelatetheintensityoftripmakingtolanduseandsocioeconomiccharacteristicsProvideameasureoftravelfrequency,yieldingatotalvolumeoftripsdefinedinagivenregionatagiventimeLand-use&socio-economiccharacteristicZoneiDependingontheactivitiesinthezone,itcanproduceand/orattracttrips.22TripClassificationbyTripPurposeTypically,aminimumofthreecategoriesareused:HBW-HomeBaseWorktrips(Residential).HBO-HomeBaseOthertrips(Residential)NHB-Non-HomeBasetrips(Non-Residential)Truck,transit,andtaxitrips(ifany)aretreatedseparately.Rcationaltrips.NHBtripsbyspecialgenerators.23TripClassificationbyTripPurpose:Example基本定义出行：从O-D的单向移动HB：O/D是家NHB：O和D都不是家出行产生：HB的家或NHB的起点出行吸引：HB的非家端或NHB的终点出行生成：产生和吸引的总和25ModelingTechniquesITE(InstituteofTransportationEngineers)TripRates.Typicallyusedfortrafficimpactanalysis(bytrafficengineers)CrossClassification(orCategoryAnalysis)UsealotfortripproductionMultipleRegressionWorkbetterasatripattractionmodel.26ITETripRatesITETripGenerationHandbookTripGentransportationsoftware(Version9)Ratesbasedupondemographics(averagehouseholdsize,businesstype,numberofemployees,etc…)AcompilationofdatafromalloverNorthAmericaonmanydifferenttypesoflandusesProductionsandattractionsforeachtypeoflandusearerelatedtosomemeasurablevariablesUsealotinsiteimpactanalysis27TripGenerationRateExampleSingle-familydetachedhousingAveragetriprateperdwellingunit调查资料整理分析北京市交通出行率指标（北京交通发展研究中心）28北京市交通出行率指标2930Cross-Classification(orCategoryAnalysis)HouseholdsinTAZsaggregatedintogroupsRatesforeachgroupusedtodeterminethenumberoftripsTripratesbasedonhouseholdcharacteristics(incomelevel,vehicleownership,householdsize,…)31CrossClassificationModelsAssumestripratesarestableovertimeDerivedfromtravelsurveysGrouphouseholdsindifferentstrataAdvantagesGroupingsareindependentoftheTAZsystemNopriorassumptionabouttherelationship(i.e.,linear)RelationshipscandifferfromcelltocellSimple,easytounderstandDisadvantagesDoesnotpermitextrapolationNogoodnessoffitmeasuresRequireslargesamplesizeNoeffectivewaytochooseamongvariablesforclassificationortochoosebestgroupsforagivenvariable32Cross-ClassificationExample(1)Thefollowingdatahasbeengatheredforathree-zonecity.DistributionofAutosperHHbyIncome

Householdswithcars(%)Zone

Household

MeanHHInc. 0123+1 4323 $40,000 101060 202 7072 $30,000 153045103 3016$20,000 20403010

33Cross-ClassificationExample(2)

PersonTripsperHHbyCarsandTripPurpose

PersonTripsbyCars

(%)bypurposeZone

MeanHHInc.0123+

HBWHBONHB1 $40,00081115 171236242 $30,00061014161834223 $20,000571012303020

Question:WhatarethetriptotalsforHBW,HBO,andNHBtripcategories?34Cross-ClassificationExample(3)TotalPersonTripsbyTripPurpose

Generated

MeanTrips

TripsbyPurposeZone

TotalTrips

perHH

HBWHBONHB1 61819

14.3

74182725512364 2 83450 11.8 1502128373200283 24128 8.0723872385791Total 169397 11.7 296785786638182 8(.1)+11(.1)+15(.6)+17(.2)=14.314.3(4323)=61819 (61819)(.12)=7418MeantripsperHHHH%bypurpose35Cross-ClassificationExample(4)Households(%),PersonTripsperHH,andTripsasafunctionofHHIncome36RegressionModelsSimpleregression:onlyoneindependentvariableMultipleregression:morethanoneindependentvariablesNumberoftrips=f(population,autos,numberofdwellingunits,…)Trippredictors(i.e.,independentvariables)needtobeindependentFactorExamplePersonalcharacteristicsGender,Age,Income,OccupationHouseholdcharacteristicsFamilysize,Carownership,Numberandageofchildren,HouseholdincomeZonalcharacteristicsLanduse,Residentialdensity,Numberofemployment,Accessibility37RegressionModelsforTripGenerationRegressionAnalysisHHTrip/Day13122325210212726310312311411414410512618812816915101638OrdinaryLeastSquaresYyieie1e2e3e4e5X(xi,yi)xi39OrdinaryLeastSquares(Cont.)Question:Intheabsenceofavalidrelationshipb/tyandx,whatisyourbestguessforanyvalueofx?40GeometryoftheRegressionLineYXDeviationoftheobservedvalueYfromtheregressionestimateDeviationoftheregressionestimatefromthemean41GeometryoftheRegressionLine(Cont.)TotalSum-of-the-Squares ObservedvaluesaboutthesamplemeanRegressionSum-of-the-Squares Estimatedvaluesaboutthesamplemean Variation“capturedby”or“explainedby” themodel’sexplanatoryvariablesErrorSum-of-the-Squares Errorterm,minimizedinOLSDefinitionExpression42GoodnessofFitSSTisfixedoncethedatasetisselected.SSEisminimumundertheOLSestimator.SSRismaximizedsinceSSTisfixed(i.e.,maximizetheexplanationpoweroftheregressionline).CoefficientofDetermination(orR2)ThecloserthevalueofR2isto1.0.,thebetterthelinearmodelisintermsofaccountingforthevariationinthedependentvariabley.43GoodnessofFit(Cont.)SimplePearsonCorrelation(standardizedmeasureofcovariance)Correlationb/tdependentvariableyandindependentvariablexMultipleR(orMultipleCorrelationCoefficient)RangeofRisgreaterthanR2sinceCOV(x,y)maybepositiveornegative.44AnalysisofVariance(ANOVA)RegressionErrorsTotalSource

Sum-of-Squares

df

MeanSS

F-Ratiokn-k-1n-1MSR=SSR/kMSE=SSE/(n-k-1)F=MSR/MSEEquivalentF-RatioSinceandGeneralrule:F≥445StandardErroroftheEstimateAnestimateofthevarianceofthepopulationyaboutthetrueregressionline.46SignificantoftheRegressionCoefficientsStandardErroroftheBivariateRegressionCoefficient(b1)iny=b0+b1xwhereGeneralrule:(95%certainthatb1exceedsapproximatelytwiceitsstandarderror)StandardErroroftheMultipleRegression(i.e.,y=b0+b1x1+…+bkxk)whereistheithdiagonalelementoftheinvertedcorrelationmatrix.Sameasabove47StepsinDevelopingRegressionModelsIdentifyindependent(causal)variable(s)Basedonexperience,judgmentandopinionsfromexpertsCollecthistoricaldataonbothdependentandindependentvariablesDependentvariables:tripproductionortripattractionIndependentvariables:socio-demographicsforproductionandlanduseandemploymentforattractionPlotandidentifythefunctionalrelationshipLinear,exponentialorpolynomialDeterminetheparametersUseanystatisticalsoftware(e.g.,MicrosoftTool=>DataAnalysis)48FunctionalFormsofRegressionModelsForm

Model

Dependent

Independent

Parametersb0,b1b0,b1,…,bnb0,b1b0,b1yyyyxx1,x2,…,xnxxSimpleMultipleExponentialNonlinear49Example1:SimpleRegressionAlanduseplannerobservedthatin5zonesofthecitythenumberofgasstations(y)inrelationtothepopulation(x)in1000wasasfollows:(x,y):(1,2),(5,7),(3,3),(2,5),(4,8)SetupalinearequationconnectingyintermsofxDetermineR2andapplythet-Test50Example1:SimpleRegression51Example1:SimpleRegression52Example1:ResultsfromExcel53Example254Example2:Results55Example2:ResultsModelComment:ModelissignificantsinceitpassesbothF-Test(F>4)andt-Test(t>2).Also,thesignofthecoefficientforLABFispositive,whichiswhatwewouldexpect.案例——绵阳出行生成绵阳出行生成模型出行产生模型——交叉分类模型；根据家庭车辆保有情况（3类），职业（4类），出行目的（6类），交叉得到每种人群的出行产生率。出行吸引模型——基于小区各类用地性质的线性模型。案例——绵阳出行生成车辆保有模型

模型建立了家庭月均收入水平和家庭小汽车保有量之间的关系。在规划年，家庭收入将随着GDP的增长而增长，因此就可以预测未来年各个收入水平下家庭小汽车的保有量。

模型将预测不同的收入水平下没有小汽车、拥有一辆小汽车和拥有两辆及以上小汽车的百分比。公式如下：P(N)i为第i小区家庭拥有N辆车的百分比;Ii为第i小区的收入；

K，b，bh为待标定的参数。案例——绵阳出行生成车辆保有模型

P(1+)=S1h/(1+exp(-LP1)S1h=saturationlevelforHH,assume1

LP=k+(b+bh)ln(y)

-19.241.58170.54961

IncomeP(1+)-surveyskbbhS1hLPexpP(1+)-modelsqerror10000.021167-19.241.58170.54961-4.51891.6551%0.00020000.045208-19.241.58170.54961-3.04120.9215%0.00030000.095776-19.241.58170.54961-2.1778.816110%0.00040000.181044-19.241.58170.54961-1.5634.775317%0.00060000.296645-19.241.58170.54961-0.6992.012333%0.00180000.507921-19.241.58170.54961-0.0861.0948%0.001100000.635964-19.241.58170.549610.38940.677460%0.002120000.64-19.241.58170.549610.7780.459369%0.002rsq0.987413

0.006调查所得的总的具有1辆及以上车的家庭收入比例模型得到对应不同收入层次拥有车辆数的比例案例——绵阳出行生成车辆保有模型

模型拟合标定拥有一辆及一辆以上车的百分比（P1+）和拥有2辆及2辆以上车在拥有1辆及1辆以上车中所占的百分比的系数（P2+），然后通过计算可得：没有车的百分比P(0),拥有一辆车的百分比P(1)和拥有两辆及以上车的比例P（2）。得到

zone平均收入ncoco1co2+1017000.0769230.5215263240.4680141850.0104594911024250.156250.6680301780.3308632210.0011066011032900.20.8981265140.101869983.5062E-0610400.9892072410.0107927592.62861E-101054250.250.6680301780.3308632210.0011066011063366.1759780.8268476350.1731125523.98132E-051073363.8636360.8268476350.1731125523.98132E-051083212.650.8268476350.1731125523.98132E-051093502.9986230.8268476350.1731125523.98132E-051102695.7227270.8981265140.101869983.5062E-061112091.2454550.8981265140.101869983.5062E-0611200.9892072410.0107927592.62861E-101132570.4824560.8981265140.101869983.5062E-061142875.3750.8981265140.101869983.5062E-061151861.50.9543809910.0456189011.07967E-0711600.9892072410.0107927592.62861E-101173136.6818180.8268476350.1731125523.98132E-0511836670.8268476350.1731125523.98132E-051192235.1439390.8981265140.101869983.5062E-061203187.750.8268476350.1731125523.98132E-05案例——绵阳出行生成出行产生模型模型具有以下计算表达式：其中：为i区出行的产生量为c类的平均出行产生率为i区c类的总人口N为交叉分类的总分类数案例——绵阳出行生成出行产生模型

模型将人群按不同的车辆拥有情况（0辆，1辆，2辆及以上），职业情况（在职，在校大学生研究生，中小学生，其他），出行目的（HBW基家工作、HBCOL基家去大学、HBSCH基家去中小学、HBSH基家购物、HBO基家其他、NHB非基家出行），由调查数据通过excel统计得到各类的出行数量除以

各分类的人数（通过excel统计可得），得到各类的出行率（去除逻辑上不可能的组合，见下页表格。）

案例——绵阳出行生成出行目的个人类型NCOCO1CO2+全家庭HBW在职1.481.521.401.49其它0.360.390.110.36HBCol在校大学生、研究生0.850.620.410.78