Abstract

App-based ride-hailing services have expanded rapidly in recent years. In this study, simulation experiments are carried out for two regions in Germany: the metropolitan Berlin area and the rural area of the district Vulkaneifel (Volcanic Eifel). Transport users’ travel behavior is fixed and the operator is enabled to adjust the ride-hailing vehicle fleet size to keep 90% of all waiting times below 10 minutes. The results show that the trip density has a major impact on the required ride-hailing fleet size as well as the resulting service (e.g. operation hours, vehicle-kilometers and pooling rate). The simulation results reveal a nonlinear relationship of trip density and ride-hailing service parameters. A linear upscaling of simulation results for a small population sample may yield an overestimation of the fleet size, operating hours and vehicle kilometers. For low trip densities, the effect of pooling (ride-sharing) is disproportionately smaller compared to larger trip densities.

Highlights

  • App-based ride-hailing services have expanded rapidly in recent years

  • The results have shown that the trip density has a major impact on the required ride-hailing fleet size as well as the resulting service

  • The simulation results confirm the nonlinear relationship of trip density and ride-hailing service parameters

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Summary

Introduction

App-based ride-hailing services have expanded rapidly in recent years. In this study, simulation experiments are carried out for two rAepgpio-nbsasiendGriedrem-haaniyli:ntghesemrveitcreosphoalivteanexBpearnlidnedarreaapiadnldy itnherercuernalt yaereaarso. fInththeisdisstturdicyt, Vsiumlkualanteioifnele(xVpoelrcimaneinctsEaifreel)c.aTrrriaendsopuotrtfoursetwrso’ trreagvieolnsbeihnaGvieorrmiasnfiyx:etdheanmdettrhoepoolpietarnatoBreirslinenaarbelaedantdo tahdejursutrathl earreiadeo-fhathileindgisvtreihcitcVleufllkeaentesiifzeel (tVookleceapnic90E%ifeolf).aTllrawnasiptoinrgt utismeress’ btrealvoewl b1e0hamviinourteiss.fiTxhede raensdultthseshoopwerathtoart itsheentraibpleddentositaydhjuasst athme arjiodre-ihmapilainctgovnehthicelereflqeueirtesdizreidteo-hkaeielipng90fl%eeot fsiazlel awsawitienlgl atsimthees rbeesluolwtin1g0smerivniuctees(e. .Tgh. eopreersautlitosnshhoowurst,hvatehthiceletr-ikpilodmenestietyrshaansdapmooaljionrgirmatpea)c. tTohne tshime urelaqtuioirnedrersiudlet-shraeivlienagl afleneotnsliizneeaarsrewlaetliloansshthipe orefsturilptindgensseirtvyicaend(er.igd.eo-hpaeirlaintigonsehrovuicres,pvaerhaimcleet-ekrisl.oAmleitneerasraunpdspcaoloinligngofrastiem).uTlahtieonsimreusulalttsiofnorreassumltsalrlepvoepalulaatnioonnlsinaemapr lreelmataiyonysiheilpd aonf torviperdeesntismityataionndorifdteh-ehflaieleint gsiszeer,voipceerpaatirnagmheoteurrss.aAndlinveeahriculpeskcialolimngetoefrss.imFourlalotiwontrriepsdueltnssfiotireas,stmhealelffpeocptuolfatpiooonlsinagm(prliedem-sahyayriinegld) iasndoivsperreosptoimrtiaotinoanteolyf tshme aflleleert sciozme,poapreerdattoinlgarhgoeurrtsriapnddevneshiitcieles.kilometers. Schlenther / Procedia Computer Science 00 (2021) 000–000 hailing vehicles This in turn improves the availability of vehicles and may reduce waiting times for all ride-hailing users. Operators of such services may use these positive economies of scale by limiting the ride-hailing service to high demand areas. Ride-hailing services with ride-sharing (pooling) allow for more flexibility in the dispatching and trip insertion algorithms. This provides a further incentive for operators to rather provide services in densely populated areas and once more raises the question of how to deal with sparsely populated regions

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