城市轨道交通线路负荷实时推算模型及控制方法

城市轨道交通线路负荷实时推算模型及控制方法城市轨道交通线路负荷实时推算模型及控制方法

摘要

城市轨道交通系统作为现代城市重要的交通工具，面临着如何降低运行成本、提升运行效率等问题。其中，线路负荷实时推算是保障城市轨道交通系统正常运行的关键，因此开展城市轨道交通线路负荷实时推算模型及控制方法研究具有现实意义。本研究从线路负荷实时推算的角度，利用机器学习和仿真技术开展城市轨道交通线路负荷实时推算模型及控制方法研究。首先，基于支持向量回归的机器学习方法，建立城市轨道交通线路负荷预测模型。其次，利用动态仿真技术获取城市轨道交通系统运营数据，验证建立的负荷预测模型的预测准确性。最后，针对实时控制问题，提出了一种基于模型预测控制的方法，在保障列车运行的安全性和稳定性的前提下，最小化列车间隔时间。仿真结果表明，本研究提出的城市轨道交通线路负荷实时推算模型及控制方法能够准确预测线路负荷变化，并实时调整列车间隔时间，提高了城市轨道交通系统的运行效率。

关键词：城市轨道交通；线路负荷；实时推算；机器学习；控制方法；动态仿真。

Abstract

Asanimportanttransportationtoolinmoderncities,urbanrailtransitsystemsfacetheissuesofreducingoperationalcostsandimprovingoperationalefficiency.Amongthem,real-timecalculationoflineloadsisthekeytoensurethenormaloperationofurbanrailtransitsystems,soitisofpracticalsignificancetocarryoutresearchonthereal-timecalculationmodelandcontrolmethodofurbanrailtransitlineloads.Inthisstudy,weusedmachinelearningandsimulationtechnologytoconductresearchonthereal-timecalculationmodelandcontrolmethodofurbanrailtransitlineloadsfromtheperspectiveoflineloadreal-timecalculation.Firstly,basedonthesupportvectorregressionmachinelearningmethod,weestablishedthelineloadpredictionmodelofurbanrailtransit.Secondly,thedynamicsimulationtechnologywasusedtoobtaintheoperationaldataoftheurbanrailtransitsystemtoverifythepredictionaccuracyoftheestablishedloadpredictionmodel.Finally,amodelpredictivecontrolmethodwasproposedforreal-timecontrolissues,andthetrainheadwaywasminimizedwhileensuringthesafetyandstabilityoftrainoperation.Simulationresultsshowthatthereal-timecalculationmodelandcontrolmethodofurbanrailtransitlineloadsproposedinthisstudycanaccuratelypredictthechangesinlineloadsanddynamicallyadjustthetrainheadway,whichimprovestheoperationalefficiencyofurbanrailtransitsystems.

Keywords:urbanrailtransit;lineload;real-timecalculation;machinelearning;controlmethod;dynamicsimulationUrbanrailtransithasbecomeanintegralpartofmodernurbantransportationsystemsduetoitshighpassengercapacity,lowenergyconsumption,andenvironmentalfriendliness.However,theincreasingdemandforurbanrailtransithasoftenledtoovercrowdingontrainsandstations,especiallyduringpeakhours.Theimbalancebetweenpassengerdemandandsystemcapacitycanresultinlongwaitingtimes,reducedoperationalefficiency,andevenpotentialsafetyhazards.

Onekeyfactoraffectingtheoperationalefficiencyofurbanrailtransitsystemsislineload,whichreferstothenumberofpassengersoccupyingacertainsectionofarailwaylineperunittime.Animbalancedlineloaddistributioncancausecongestion,delays,anddecreasedefficiency.Therefore,accuratereal-timecalculationanddynamicadjustmentoflineloadsarecriticalforensuringsafeandefficientoperationofurbanrailtransitsystems.

Inthisstudy,areal-timecalculationmodelandcontrolmethodforurbanrailtransitlineloadswereproposedbasedonmachinelearningtechniquesanddynamicsimulation.Theproposedmethodcanaccuratelypredictchangesinlineloadsandadjustthetrainheadwaybasedonthesepredictionstomaintainstabilityandsafety.

Theproposedmodelusesreal-timedataonpassengerdemand,trainschedules,andtrainarrivaltimestopredictlineloadsforeachsectionoftherailwayline.Thepredictionmodelisbasedonmachinelearningalgorithms,suchasneuralnetworks,thatcanlearnfromhistoricaldataandadapttochangingconditions.

Thecontrolmethodusesthepredictedlineloadstodynamicallyadjustthetrainheadway,whichreferstothetimeintervalbetweenconsecutivetrains.Byadjustingthetrainheadway,thesystemcanbalancelineloadsandminimizethewaitingtimeforpassengerswhileensuringsafeandstableoperation.

Toevaluatetheeffectivenessoftheproposedmethod,adynamicsimulationwasperformedusingareal-worldurbanrailtransitsystem.Thesimulationresultsshowedthattheproposedmethodcanaccuratelypredictlineloadsandadjustthetrainheadwayinreal-timetomaintainstabilityandsafetyofthesystemwhileimprovingitsoperationalefficiency.

Inconclusion,thisstudyproposesareal-timecalculationmodelandcontrolmethodforurbanrailtransitlineloadsbasedonmachinelearninganddynamicsimulation.Theproposedmethodcanaccuratelypredictlineloadsanddynamicallyadjustthetrainheadwaytobalancepassengerdemandandsystemcapacity,therebyimprovingtheoperationalefficiencyofurbanrailtransitsystemsFurthermore,theproposedmodelandmethodcanalsoenhancethesafetyandstabilityoftheurbanrailtransitsystem.Bypredictingandadjustingthelineloadsinreal-time,thisapproachcanpreventoverloadingandreducethechanceofaccidentsrelatedtotraincongestion.

Themodelandmethodcanalsoprovidevaluableinsightsintothesystem'sperformancebygeneratingdataonpassengerdemand,trainschedules,andsystemcapacity.Thisinformationcanbeusedtooptimizethetransitsystem'sdesignandoperation,suchasdeterminingtheoptimalnumberoftrains,trainfrequency,orstationcapacity.

However,somechallengesneedtobeaddressedtoimplementthisapproachsuccessfully.Onecriticalissueisdataquality;theaccuracyofthemodelandmethodishighlydependentonthequalityoftheinputdata,suchaspassengerdemandandtrainlocations.Therefore,itisessentialtoestablisharobustdatamanagementsystemthatcanensuredataaccuracy,completeness,andtimeliness.

Anotherchallengeisthehighcomputationalcomplexityofthemodelandmethod,whichmayrequiresubstantialcomputingresourcesandprocessingtime.Therefore,itisessentialtodevelopefficientalgorithmsandoptimizehardwareandsoftwareconfigurationstoensurefastandaccuratecalculations.

Insummary,thereal-timecalculationmodelandcontrolmethodforurbanrailtransitlineloadsproposedinthisstudyoffersapromisingsolutionforimprovingtheefficiency,safety,andstabilityofurbanrailtransitsystems.Bycombiningmachinelearninganddynamicsimulation,thisapproachcanaccuratelypredictlineloads,dynamicallyadjusttrainheadway,andprovidevaluableinsightsintosystemperformance.However,somechallengesneedtobeaddressedtoimplementthisapproachsuccessfully,suchasdataqualityandcomputationalcomplexityOnechallengeisensuringtheaccuracyandavailabilityofdata.Machinelearningmodelsrelyheavilyonthequalityandquantityofdatainputs.Inaccurateorincompletedatacanreducetheaccuracyofthemodel'spredictionsandrecommendations.Therefore,itisimportanttoestablishreliabledatacollectionprocessesandsystems.Thismayinvolveinstallingsensors,cameras,andotherdata-gatheringequipmentthroughouttherailtransitsystem,whichcanbecostlyandtime-consuming.

Anotherchallengeisthecomputationalcomplexityofthesystem.Theproposedapproachinvolvesprocessinglargeamountsofdatainreal-time,whichrequiressignificantcomputationalpower.Thismayrequiretheuseofhigh-performancecomputingsystemsorcloudcomputingservices,whichcanbeexpensiveordifficulttoimplement.Additionally,theprocessingpowerrequiredmayvarydependingonthesizeofthetransitsystem,thenumberoftrainsandpassengers,andotherfactors.

Furthermore,theimplementationofthisapproachmayrequirechangestoexistingtransitsystemsandinfrastructure,suchastheinstallationofcommunicationsystemsorthemodificationoftrainschedules.Thismayrequiresignificantcoordinationandcooperationbetweentransitauthorities,stakeholders,andthepublic.Additionally,theremayberesistancefromtrainoperatorsorpassengerswhoareaccustomedtotraditionalschedulingandmaybereluctanttochangetheirhabits.

Inconclusion,theproposedapproachofusingmachinelearninganddynamicsimulationtopredictandadjustrailtransitlineloadshasthepotentialtoimproveefficiency,safety,andstabilit