A Review on Estimation of the Capacity at Urban Arterial

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 09 Issue: 08 | Aug 2022 www.irjet.net p-ISSN: 2395-0072

A Review on Estimation of the Capacity at Urban Arterial

1M.Tech. (Civil) Transportation Engineering, BVM Engineering College, Vallabh Vidyanagar, Gujarat

2Head of Department, Civil Engineering Department, BVM Engineering College, Vallabh Vidyanagar, Gujarat

3Assistant Professor, PG Coordinator Transportation Engineering, Civil Engineering Department, BVM Engineering College, Vallabh Vidyanagar, Gujarat

4Assistant Professor, Transportation Engineering, Civil Engineering Department, BVM Engineering College, Vallabh Vidyanagar, Gujarat ***

Abstract - The scenario of traffic in India is extremely heterogeneousduetoseveraltypesofvehiclesoperatingwith variouslanebehavioranddriverbehavior,resultinginhighly heterogeneous character due to their static and dynamic characteristics. Currently, traffic on the road is rising exponentially, and the amount of traffic exceeds the acceptablelimit.Fortheplanning,design,andmanagementof roadway infrastructure, a deep understanding of several aspects of roadways is important. It is necessary to have a betterhighwaysystemwithmorecapacityinordertoenhance vehicle traffic. In India, highways usually operated in mixed traffic environments, with massive differences in driving behavior from one location to the next. The variety of influencingelementspresentonahighwaysignificantlyaffects the traffic flow stream, and therefore capacity becomes unpredictableinmixedtraffic.Anotheraspectthatdetermines the overall operating characteristics and capacity of a multilane roadway is vehicle flexibility. It will be easy to analyze and estimate the diverse traffic behaviors of the region by considering the appropriate traffic flow characteristicslikespeed,density,andflow inthemixedtraffic at urban roadnetworksusingsimulationsoftwarelikeVISSIM.

Key Words: Traffic volume, Capacity, Traffic Simulation, VISSIM, Urban Roads

1. INTRODUCTION

Indevelopingnations,thetransportationsystemiscrucial becauseitconnectscommoditiesandpassengersfromone region to another. Road transport does have a greater portioninthetransportationsystemthananyothermodeof transportation.Roadtransportationdeliversthemajorityof passengers and freight. In a growing country like India, peopleuseroadtransportationmorethananyothermeans of transportation. With a total length of 6,215,797 kilometers, India has the world's second-largest road network.Abettersystemfordeliveringproductsandpeople across the country is required due to the significant expansioninvehiculartrafficgrowthonroadways.Because ofitsquicksocialandeconomicdevelopment,India'straffic volumeislikelytoexpandinthecomingyears.Recognizing this, the Government of India (GOI) has taken new steps

undertheNationalHighwayDevelopmentProgram(NHDP) to provide high-density corridors, such as multilane highwaysandexpressways,tomeetthecurrentandfuture demands for rapid, efficient, safe, and interconnected highway networks. When it comes to traffic flow and capacity, multilane roadways require attention. On such roads,avarietyoflaneswitchingandovertakingmaneuvers occurs, some of which result in serious accidents. The behavior of traffic flow on multilane highways is a complicatedphenomenonthatrequiresamoreconceptual and logical approach to data collection, analysis, and interpretation. Various attempts to model and quantify traffic flow behavior by utilizing empirical and analytical approaches,eveninmixedtraffic, havebeeneffective,but they are confined to field particular roads and traffic circumstances.Practicalproblemsinperforminghugefield testsundervastfluctuationsoftrafficflowcharacteristics, non-availabilityofessentialfieldconditions,anddifficultyin testing with individual components are all factors that contributetothelimits.Computersimulationhasshownto beausefultoolinreplicatingcomplicatedtrafficsystemsand allowing experimenting with the fundamental traffic flow systemasasolutiontotheserealdifficulties.VISSIMatraffic simulationmodelbuiltinGermanybasedonWiedemann's researchworkoncarfollowingbehavior,isoneexample.

Now India contains the second-largest population in the worldandisadevelopingnation.Therefore,theincreased traffic on the roadways is significantly impacted by these higher population numbers. There are now multi-lane metropolitan roadways with four, six, and eight lanes in industrialized nations like the developed countries. However,duetotheunevendistributionoftrafficonIndian highways, there are few of these kinds of roads here. The trafficonIndianroadwaysisaffecteddifferentlybydifferent types of non-motorized vehicles including bicycles, hand carts,animalcarts,andsoon.Thesenon-motorizedvehicles include two-wheelers, three-wheelers, cars, buses, light commercial vehicles and high commercial vehicles. ComparingtootherIndianhighwaysystems,thetrafficon urban roadways is different. Because of the mixed traffic conditions on urban highways,Vehicle mobility and accelerationareslowerthanonanyothertypeofhighway

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infrastructureduetomixedtrafficsituations.Therefore,it's indeed crucial to properly evaluate and analyze the characteristics of traffic on India's urban roadways. In addition,thereareavarietyofcarsontheurbanroad,some ofwhichmovequicklyandothersslowly.Whenurbanroads have multilane featuresthe traffic composition, vehicle speeds, and other factors become extremely important becauseitmakesthingseasiertodefinethedeliveringhigh quality, capacity, and level of service. The design and implementationofthehighwaysystemmustbeginningwith alltheseconsiderations.Now,incitiesheaviervehiclesmove lessslowlyandhaveagreatersurfaceareaontheroad.The trafficflowonurbanroadwaysissignificantlyimpactedby thesekindsofvehicles.

1.1 Need of Study:

Onmultilanehighways,itisinneedoffurtherresearchon capacityandtrafficflowbehaviour.Carfollowingandlane switching behaviour modes were established for homogeneoustrafficscenariosinindustrialnations,butthey donotinstantlyadapttomixedtrafficcircumstances.India's population growth and urban area expansion have contributedtoagreaterexpansionoftrafficinurbanareas. Asaconsequence,thereisachallengewithtrafficcongestion onurbanhighways.Urbanroadwaysexperienceavarietyof trafficconditionsandeachurbanregionhasadifferentkind ofcongestionproblem.Forimprovedurbanroadplanning, design, and operation, hence it is important to analyse it. Frequent lane changes, vehicle overtaking, and individual vehiclespeedalwayshaveanimpactonastretchofhighway capacity.Thespeedonurbanroadsisslowerthanonrural roadsandthetrafficdensityonurbanroadsishigherthan theruralroads.Thetrafficconditionsonurbanroadwaysare influenced by different vehicle categories. Due to their slower on-road movement and higher on-road area occupancy compared to other classes of roadsheavy vehiclesnow have a considerable influence on traffic in urbanareas.Heavyvehiclebehavioursuchasthisaffectsthe capacity of the traffic stream and passenger car units of vehicles more. Studying the features of urban road traffic andalsohowheavyvehicleuseaffectsitisthereforecrucial.

1.2 The objective of the Study:

Toanalyzethemixedtrafficflowcharacteristicsatthe urbanmidblocksectionundermixedtrafficcondition. 

ToderivethedynamicPCUvaluesfordifferenttypesof vehiclesunderthemixedtrafficcondition. 

To study the effect of mixed traffic on variation of capacityofurbanmidblocksection. 

To calibrate driver behavior model parameters of VISSIMsoftwaretoreplicatemixedtrafficconditionon urbanmidblocksection.

2. Literature Review

Arpan Mehar, Satish Chandra and Velmurugan, S. (2014). In this study, the VISSIM software is used to determinehighwaycapacityundermixedtrafficconditions. TheauthorsofthispaperconcentrateontheuseofVISSIM softwaretocapacityestimationofmultilanehighwayswith mixed traffic flow. Data on traffic flow collected from a segment of divided four-lane highway is used to build the speed-flowcurve.Thesimulatedspeed-flowcurveandthe field curve are compared by using same set of field data through VISSIM. The speed and capacity of the roadway were found to be overestimated by VISSIM in its original form.Theapproachwasdemonstratedtofunctiononafourlanedividedhighwaywithpavedshoulders,withsimulated capacityof5329pcu/hragainst5277pcu/hrinthefield.To determine CCO and CCI for mixed traffic conditionsbase valuesofCCOandCCIcomputedforhomogeneousstream are used. This study shows that the weighted average approach for calibrating CCO and CCI for field observed trafficcompositioniseffectiveinmakingsimulatedcapacity witha2percenterrorwhencomparedtocapacityderived fromthefielddata. 1

Arpan Mehar and Seelam Mehar. (2017) Inthisresearch paper, the traffic flow data is generated using the microscopic traffic simulation flow model to get essentialparameters.InputstothesimulationmodelVISSIM areanalysedusingfielddataobtainedatfour-lanehighway sections.studycalibratedandvalidatedtheVISSIM model based on the field data Through trial and error and evaluatingvariousinfluencingsimulationfactorsincluding RSN (random seed number), limitations of desired speed, outputvolume,traveltime,etc.Trafficvolume,speed,and capacityareutilisedtocalibratethemodel uptoa certain levelofsatisfactionandserveasmeasuresofeffectiveness. According to the study, the number of lane changes is influencedbyboththevolumeoftrafficandthenumberof lanes available in each direction of travel. On simulated segmentsoffour-lane,six-lane,andeight-lanedividedroads, themaximumnumberoflanechangesandlanechangesat capacitylevelofvolumearealsoevaluated.Whentrafficflow hitsitsmaximumlevel,itisdeterminedthatnofurtherlane changes are seen in any of the simulated segments. The relationshipbetweencapacityperlaneandthenumberof lane changes has been establishedand it reveals that capacity decreases as the number of lanes increases. field datainputsthatwereasneartothefieldcapacityaspossible were used to generate the simulation. It was only 1.25 percentdifferentincapacity. 2

S. Velmurugan, Errampalli Madhu, K. Ravinder, K. Sitaramanjaneyulu and S. Gangopadhyay (2010) Inthis study, based on traditional and microscopic simulation modelsfree speed profiles and speed-flow equations for variousvehicletypesforvarioustypesofmulti-laneroads

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have been developedand as a consequenceroadway capacity has been evaluated. Additionally, several studies have been conducted on the lane change behaviors of various vehicle typesand their effects on highways with multiplelaneshavebeenthoroughlyassessedandtheDesign ServiceVolumeforseveraltypesofdividedcarriagewayshas beendevelopedforIndia'smulti-laneroads,includingfourlane,six-lane,andeight-lanelanes,withareasonablelevelof accuracy for the current diverse traffic conditions. It was foundthatforvariouspeakhourratiosof7to10%,theDSV underLOS-BandLOS-Cofafour-lanedividedcarriageway varies from 22296 to 47777 PCUs/day/direction on plain terrain. Similar values are found for six-lane divided carriageways(30932to66283PCUs/day/direction)andfor eight-lane divided carriageways (39184 to 83966 PCUs/day/direction) for various peak hour ratios of 7 to 10%onplainterrains.Itwilloftennothavebeendesirable to implement LOS-C on multi-lane highways. In order to upgrade the facility to a six-lane divided carriageway, the DSV values that correlate to LOS - C may be used to determine the holding capacity of four-lane divided carriageways. Similar to this, the DSV values obtained for LOS-C may be used to determine the holding capacity for wideningasix-lanedividedhighwaytoaneight-lanedivided highway.

Ravikiran Puvvala, Balaji Ponnu and Shriniwas S Arkatkar (2013) In this research paper the highway capacity is estimated using the micro-simulation model, VISSIMforIndianExpressways.Inthisstudytoestimatethe capacity for eight lane urban expressway, Delhi–Gurgaon expresswaywasselected.Thepurposeofthisresearchisto usetheVISSIMmicro-simulationmodeltomodeltrafficflow on Indian urban expressways with a focus on the DelhiGurgaonexpressway.ThemicrosimulationmodelVISSIMhas beenfoundtobesuitableforsimulatingandhenceanalyzing heterogeneous traffic flow in expressways with statistical relevance, according to this study. When input flow is providedevery15minutesandoutputiscollectedevery30 minutes,thesimulatedflowandspeedfromthecalibrated model meet the observed data the closest, however the capacity value predicted from the model is closer to the observedvaluewhentheoutputistakeneveryminute.This makessensebecausewhenthelevelofperiodaggregationis reduced,lowerspeedsandlowerflowswouldbedetectedat more frequencies. Therefore, a more fully-developedflowspeed curve results when the interval for consideration is small,lowerand higher mean speeds as well aslower and higher flow levels are recorded. Based on the observed vehicle class mix during the research period, the capacity calculatedfromthisstudyis10560vehiclesperhourinone directionoftravelfortheDelhi-GurgaonExpressway.[4].

Pratik U. Mankar and Dr. B.V.Khode (2016) Inthisstudy the highway capacity of urban roads is estimated by the green shield model and results are compared with the

Microscopicsimulationmodel.Theintensionofthisresearch is to analyze the capacity for urban roads in mixed traffic condition. It has been found from this study is the sudden increase in width of lane on the road the PCU values and trafficcompositionarealsoincreasesaswellasthehighway capacityoftheroadincrease.Theobservedfieldcapacityand simulatedcapacityfoundtobe5%.[5].

Partha Pratim Dey, Satish Chandra; and S. Gangopadhyay (2008) In this study, the simulation programisdevelopedtosimulatethetrafficflowonatwolaneroad.Atwo-laneroad'scapacityinanall-carsscenario is calculated as 2,860 PCU/h. When there are just twowheelers,thespeedrisesto5,600vph,andwhenthereare onlytractors,itfallsto580 vph.Additionally,thecapacity decreaseslinearlywhenthedirectionalsplitdeviatesfroma splitof50/50.Furthermore, ithasbeenfoundthata twolaneroad'scapacityreduceswhentheproportionofheavy vehicles , tractoror three-wheelervehicles grows in the stream of traffic. However, it was discovered that the capacityoftwo-wheelersincreasedinaccordancewiththeir ownproportion.Thisisattributedtotwo–wheelersbecause ofthesmallsizeandtheirmaneuverability.Similartothis, PCUforaparticularvehicletypefallsasitsproportionofthe traffic stream and volume-to-capacity ratio increase. For two-wheelers, three-wheelers, and tractors, the high PCU valueisshownatextremelylowvolumetocapacityratios and proportions of these vehicles in the traffic stream. However,thistendencyisnotseenforheavyvehicles,where PCUincreaseswithitsownproportioninthetrafficstream ratherthandecreasingwithvolume-capacityratio.Itmight be attributable to these vehicles' largesize and poor operatingeffectiveness.[6].

Puvvala, R., Ponnu, B., Arkatkar, S., & Velmurugan, S. (2013). In this research paper the highway capacity is estimatedforeightlanedividedurbanexpresswayunderthe mixedtrafficcondition.Inthisstudytoestimatethecapacity foreightlaneurbanexpressway,Delhi–Gurgaonexpressway was selected. The purpose of this research is to use the VISSIM micro-simulation model to model traffic flow on IndianurbanexpresswayswithafocusontheDelhi-Gurgaon expressway. The simulation model is calibrated and validated using field data obtained on expressway traffic flowparameters.Theverifiedsimulationmodelisthenused to calculate Passenger Car Unit (PCU) values and capacity values for various vehicle types. Under an uncongested environment,thePCUvalueforeachofthevehicletypesfalls asvehiculartrafficincreases.Itwasalsodiscoveredthatin crowded conditions, the PCU value for heavy and bigger vehicleclassesisgreaterthantheirrespectivePCUvalues nearcapacity.Accordingtothespeed-volumecurvecreated using the simulation model, the capacity of an eight-lane dividedurbanexpresswayinflatterrainwitharoadspaceof 14.0mwideisapproximately9700PCU/hforonedirection of traffic flow. The calculated PCU values of the various

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vehicletypesaredeterminedtobeaccurateata5%levelof significance.[7].

Raju, N., Chepuri, A., Arkatkar, S., & Joshi, G. (2020). This researchpaperaboutthesimulationstudyforimprovingthe trafficflowefficiencyofanintersectioncoupledwithBRT.In thispapertheauthorfocusesonincreasingthetrafficflow efficiency of the Bhathena crossroads in Surat, which is connected to the BRT system. The study intersection is modelled in microscopic simulation software VISSIM 8.0 using field data. Based on this, a simulation model is calibratedandvalidatedbycomparingtheprobevehicle's travel time with recorded queue lengths, as well as other observabledataandsimulationresults,insuchawaythat theabsolutepercentageerroris restrictedto15%.Traffic signaldesigniscarriedoututilisingWebster'stechniqueof signaldesigninordertoenhancetrafficflowbehaviourat the intersection in terms of minimising travel time. The design of 2-phase, 3-phase, and 4-phase signal systems is one of the eventualities. The signal design is carried out usingtheWebsterapproachineachscenarioandthesame traffic signal system is simulated using the VISSIM model withthetraveltimeresultsacrossthearmscomparedtothe basecase.The3-phasesystemwasfoundtobeadequatefor thejunctionwhenconsideringBRTSoperation.[8].

Arkatkar, S., Velmurugan, S., Puvvala, R., Ponnu, B., & Narula, S. (2016). Inthisresearchpaperauthorgivesthe methodology for simulating heterogeneous traffic on expressways in developing countries. For simulating heterogeneous traffic on expressways the Delhi–Gurgaon expresswayisselected.Inthisresearchauthorsintendsto studytrafficflowonIndianurbanexpresswayswithafocus on the Delhi–Gurgaon expressway and estimate capacity usingaVISSIMsoftwaremicro-simulationmodel.Fielddata ontraffic-flowcharacteristicsonexpresswayswasutilizedin the calibration and validation of the simulation model for thispurpose.Theverifiedsimulationmodelwasthenusedto generateessentialtraffic–flowrelationships,suchasspeed–flow,speed–areaoccupancy,andflow–areaoccupancyforall traffic-flow levels from near-zero to the facility'scapacity. Thecapacityofaneight-lanedividedurbanexpresswayon flatterrainwitha14-meterwideroadarea(onedirectionof trafficflow)wasdeterminedto be between 9700vehicles perhour.[9].

Chepuri, A., Raju, N., Bains, M. S., Arkatkar, S., & Joshi, G. (2018). This paper reviews the literature on the performanceofanurbancorridorusingmicroscopictraffic simulationmodelundermixedtrafficenvironmentinIndia. The purpose of this research is to assess traffic flow characteristicsonabusyroadsegmentofanurbanarterial corridor with BRT system in Surat, which covers five crossings.UsingthemicroscopicsimulationmodelVISSIM the paper attempts to examine the delays caused to both BRT buses and motorised vehicle traffic at crossings. The

purpose of this study is to use simulation as a tool for enhancing the performance of an urban arterial corridor. Theeffortfocusesontheconstructionofthestudystretch's microscopicsimulationmodel,aswellasmodelcalibration andvalidation.Asaconsequence,thebuiltsimulationmodel was used to assess the feasibility of different traffic management methods that might reduce delay and travel timeforbothBRTbusesandprivatevehiclesinmotorised lanes, potentially lowering emissions. Furthermore, it is emphasised that the overall performance of the urban corridor's transportation system may be enhanced by applyingwell-testedtrafficmanagementsolutionsthatare pre-simulated.[10].

SatishChandraandUpendraKumar(2003)Thisstudyhas demonstrated how lane width affects the PCU for various vehicleclassificationsandconsequentlythecapacityofatwolane road. Data were collected at ten sections of two-lane roadsindifferentpartsofIndia.Ithasbeendiscoveredthat as lane width increases with the increases PCU for a particularvehicletype.Thecarriageway'swidthvariedfrom 5.5 to 8.8 metres. At each road stretch, the PCUs of all the vehicles were estimated which were classified into nine separatecategories.Thisisexplainedbythefactthatwider roads allow for more mobility andas a resulthave higher speeddifferencesbetweencarsandothertypesofvehicles. Withincreasedcarriagewaywidth,atwo-laneroad'scapacity alsoincreases.Itappearsthatlanewidthhasalinearimpact onPCU.However,theslopeofthelinearityvariesdepending onthekindofvehicle.2818PCU/histhecalculatedcapacity ofa7.2mwideroad.[11].

Menneni, S., Sun, C., & Vortisch, P. (2008). This paper reviews the Microsimulation calibration using speed-flow relationships.Inthispapertheauthorsdiscussedabouthow to make microsimulation model using the speed � flow relationship graphs from field data. A microsimulation calibrationprocessbasedonfieldandsimulationspeed-flow graphsisdescribed.Patternrecognitionalgorithmsareused to evaluate and execute matching speed-flow graphs. An EvolutionaryAlgorithmwasusedtoapplytheconcepttothe US101highwaynetworkinSanFrancisco,California.When comparedtoexistingcapacity-basedcalibrationapproaches, themethodologyimprovesthem.Asmall-scaletestnetwork simulation model was also created to help with the deploymentofthistechniqueformassivesimulationmodels. Thetestnetworksimulationmodelperformssimilarlytothe US101simulationmodel.[12].

Chepuri,A., Kulakarni,R.,Singh Bains, M., Arkatkar, S.,& Joshi E, G. (2015). This paper reviews the Evaluation of BRTSCorridorinIndiaUsingMicroscopicSimulation.Inthis papertheauthorstalksdiscussabouttheevaluationoftraffic flow of BRTS corridor in Surat city. The purpose of this researchistoassesstrafficflowcharacteristicsalonga1.8kilometer BRTS path in Surat. which comprises four

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junctions.Thereisalimitedliteratureontheevaluationof hybrid BRTS in Indian traffic. Using the microscopic simulationsoftwareVISSIM,theeffortintendstoassessthe trafficdelaysgeneratedatthesecrossings.Theprojectalso involvesaBRTSsystemperformancestudy,whichincludes aninquiryintothereasonsofdelaysandtheiroverallimpact on the BRTS. The research is being carried out in orderto recommend realistic traffic management techniques that might reduce delays and travel times for both BRTS buses andprivatetraffic,resultinginadecreaseinemissions.[13].

Raj G., G., Sekhar, C. R., & Velmurugan, S. (2013). This paperreviewstheliteratureontheMicrosimulationbased PerformanceEvaluationofDelhiBusRapidTransitCorridor. InthispapertheauthorsdiscussedaboutBusRapidTransit System (BRTS) is the most popular form of rapid public transit system. In this study, a quantitative performance strategybasedonmicro-simulationwasdevelopedwiththe objective of analysing the open system BRT corridor's performanceinDelhi. TheAmbedkarNagartoMoolchand BRTsectioninDelhiisnowoperationalfor5.8kilometres. AmbedkarNagar,SheikhSarai,PressEnclave,ChiragDelhi, Siri Fort, and G K-I Intersection were among the six main intersectionswhere16-hourClassifiedTrafficVolumedata was obtained. For bus and vehicle modes, GPS-based Velocity-Box (V-Box) apparatus was used to gather speed and delay data. To replicate real traffic activity on the researchcorridor,dataonsignaltimingandsignalphasing wascollectedatthejunctionsdescribedabove.TheMixed Vehicle(MV)laneoftheDelhiBRTcorridorhasacapacityof 3607 PCU/hr, while the Bus lane has a capacity of 1701 PCU/hr.Forbothapproaches,microsimulation-basedtraffic flowmodelswere built with the goal of achievinga wider variety of speed-flow relationships. The models that were created were calibrated and verified. The Golden Section approachisusedtooptimisethelinkperformancefunctions designedfortheMixedVehicle(MV)laneandBuslaneofthe Delhi BRT line. The ideal point, V-C for the MV Lane and 0.640fortheBusLaneoftheDelhiBRTpath,wasfoundto be0.677,withtheassociatedtrafficvolumesof2438PCU/Hr and1120PCU/HronthecorrespondingMVandBRTlanes, respectively.Thecreatedmodel'sGEHstatisticis4.13,which is less than 5, and the percentage error in capacity estimationis3.49percent.Bothofthesefindingsshowthat micro simulation is an effective way for testing the BRTS. Morecalibrationcriterianeedtoberesearchedtoimprove theaccuracyofthemicrosimulationmodelsforIndianBRT networks.[14].

Mohammad Mardani Nokandeh1, Indrajit Ghosh and Satish Chandra (2016) Inthisresearchpaperthehighway capacityisestimatedfortwolaneinterinterurbanroad.The purpose of this research is to propose a model which provides a quick estimation of capacity by removing theComplexities Thebenefitoftheproposedmodelisthatit provides a rapid and accurate prediction of the two-lane

interurban road capacity rather than requiring a timeconsumingdataextractionandanalysisprocess.Usingthis approach,itwouldbereasonabletoevaluatethesuitability ofanytwo-lanefacilityanddeterminewhetheritneededto beimproved.Themodelusedinthisstudyisdesignedfor basic7.0m-widetwo-laneroadwayswithearthenshoulders on both sides of the road. It was discovered that capacity increases as operating speed increases.The minimum capacityincreasewasdiscoveredtobe75PCU/hrforevery 2km/hrincreaseinoperatingspeed.[15].

3. CONCLUSIONS

Allofthesestudiesshowthesignificanceofcapacityforthe trafficstudiesandthemicroscopictrafficsimulationmodel VISSIM.Thecapacitymajorconceptforthetrafficstudyisto understandthetrafficcharacteristics.Ontheurbanroads,the Capacityisaffectedbyfactorslike,LanewidthandWidthof shoulder, Lateral clearance, Commercial vehicles, Road alignment and geometry (curves, Super elevation etc), Existence of intersections, One wayor twoway traffic and numberoflanes,Driversandvehicularcharacteristics,Speed regulation, Heterogeneity of traffic, etc. These studies also help to understand the effect of lane width, lane change behavioroncapacityineachclassofvehiclecategories.This study shows how to calibrate data and data validation in VISSIM and simulation is best way to understand traffic parameter. So, this study method was also used to understand the capacity of multilane highways in urban roadswiththemixedtrafficcondition.

REFERENCES

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[5] Menneni, S., Sun, C., & Vortisch, P. (2008). Microsimulation calibration using speed-flow

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[8] Chepuri,A.,Raju,N.,Bains,M.S.,Arkatkar,S.,&Joshi,G. (2018). Examining performance of an urban corridor usingmicroscopictrafficsimulationmodelundermixed traffic environment in India. European TransportTrasportiEuropei,69,1–21.

[9] Raj G., G., Sekhar, C. R., & Velmurugan, S. (2013). MicrosimulationbasedPerformanceEvaluationofDelhi Bus Rapid Transit Corridor. Procedia - Social and Behavioural Sciences, 104, 825–834. https://doi.org/10.1016/j.sbspro.2013.11.177

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[13]Arkatkar, S., Velmurugan, S., Puvvala, R., Ponnu, B., & Narula, S. (2016). Methodology for simulating heterogeneous traffic on expressways in developing countries:AcasestudyinIndia.TransportationLetters, 8(2), 61–76. https://doi.org/10.1179/1942787515Y.0000000008.

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