Dynamic Fleet Management for Cybercars

Transcription

1 Proceedngs of the IEEE ITSC IEEE Intellgent Transportaton Systems Conference Toronto, Canada, September 17-20, 2006 TC7.5 Dynamc Fleet Management for Cybercars Fenghu. Wang, Mng. Yang, Ruqng. Yang Abstract One of the major ssues n Intellgent Transportaton System(ITS) s fleet management. Ths paper proposed a dynamc fleet management algorthm for Cybercars, whch are road vehcles wth fully automated capabltes. A fleet of such vehcles forms a transportaton system called CTS (Cybernetc Transportaton System), for passengers or goods. Ths paper presented an archtecture for CTS operaton and fleet management. An algorthm based on Queung Theory s proposed for dynamc fleet management of cybercars, whch can respond to random requests. Smulaton experments demonstrated the effectveness of the proposed algorthm. C I. INTRODUCTION YERCARS are Intellgent Transportaton Systems based on road vehcles wth fully automated capabltes, or CTS(Cybernetc Transportaton System)[1]. These vehcles form a transportaton system, for passengers or goods, whch are under control of a central management system n order to meet partcular demands n a partcular envronment. Ths transportaton system may be a soluton of a publc transportaton system n specfc areas and complement the mass transt transport, provdng passenger servce for any locaton at any tme. Approprate fleet management algorthm s vtal to the effcency of Cybercars. Passengers demands are met by the management of fleet. At the same tme, the cost of fleet operaton can be decreased by usng a proper fleet management. In the past few years, there has been a rapd growth n nformaton and communcaton technologes. These advances afford opportuntes for usng real-tme nformaton to enhance the performance of decson systems n the area of vehcle routng and dspatchng. Events that occur n real tme, such as new passengers comng, unexpected delays, accdents, etc., request an algorthm whch can respond these changes tmely. Meanwhle, consderng the cost, mnmzng the vehcles n workplace s expectng. In ths work, we propose an approach for Cybercars fleet management, whch can respond new requests n a dynamc envronment, and we examne dfferent ways of mplementng t n smulaton. Several authors have nvestgated the dynamc fleet management problems recently n varous envronments: Powell et al. addressed the dynamc vehcle allocaton problem (DVA) for whch a fleet of vehcles s assgned to a set of locatons wth dynamcally occurrng demands. Most effectve DVA models are of a multstage stochastc programmng type. Regan et al. evaluated vehcle dverson as a real-tme operatonal strategy for smlar truckload pckup and delvery problems(tpdp) and nvestgated varous local rules for the dynamc assgnment of vehcles to load under real-tme nformaton. Jan Yang et al. ntroduced a new optmzaton-based polcy (OPTUN) for the TPDP, n ther paper, each truck can carry only one job at a tme and cannot serve another job untl the current job s delvered to ts fnal destnaton. Smple fleet management method, whch s easy to mplement and more robust to dsturbances and more tme-savng, s keen n realty. The algorthm presented n ths paper treats the mplement of Cybercars, where vehcles work n a roundng and un-drectonal envronment. The major contrbuton of our research s the proposal of an algorthm whch usng queue management technque to respond requests come by chances. Ths algorthm can meet passengers demands tmely, at the same tme vehcles can be sent only when requests occur. The remanng part of ths paper s organzed as follows. In the next secton, the model for the transportaton system s gven. We present an archtecture of the Cybercars fleet management n Secton 3. Secton 4 deals wth the algorthm of fleet management, the focus of our paper. The smulaton study wth numercal results, based on the case study, s the subject of Secton 5. Fnally, we summarze our man conclusons and gve some drectons for future research(sect. 6). II. MODELING AND ASSUMPTION We consder a closed rnged route wth several statons. One staton(staton 0) s the garage(see Fg.1). The clockwse /06/$ IEEE 1246

2 drecton s the postve travel drecton for each vehcle. There s a queue of watng passengers n each staton. The objectve of fleet management s to decrease the watng tme of passengers on statons. Meanwhle, vehcles n use should be as few as possble so as to cut down the runnng cost of fleet. The objectve functon can be defned as n m wt vn j Mnmze 1 j 1 T T Where wt = the wat tme of passenger vn = number of vehcles n use n a certan perod j T = the whole perod of transportaton = wegh of the frst part n the objectve functon = wegh of the second part n the objectve functon Fg.1 Rnged route and statons locaton Runnng n a rnged route, a vehcle bounds to staton has to pass through staton 0 to staton -1, ths makes up of the restrctve condton for the problem. We can defne t as 1 t0 t k, k 1 k 0 t Where t = the tme when a vehcle arrves at staton t 0 = the tme when a vehcle comes out from staton 0(the garage) t = the tme a vehcle spends n the secton between k, k 1 staton k and staton k+1 The state of each vehcle can be dvded nto full loaded and avalable, the avalable vehcle has the ablty to load passengers. Avalable vehcles can be nserted nto a set whch makes up of an avalable vehcles queue. The core of fleet management algorthm n ths paper s the schedulng of avalable vehcles n the queue. Vehcles n the avalable queue can be separated nto two parts, one s those empty vehcles n the garage whch have not been sent, the other ncludes those have been out of garage and avalable. We use the followng notaton: = ndex (= 0,1 4) st = staton (staton 0 s garage ) veh = vehcle n = full loaded passengers number per vehcle QueueVeh = queue of avalable vehcles QueueSt = queue of watng passengers n staton CQueueSt = copy of QueueSt servesct = the crcutry between st and st -1 (=1,2 4) Defnton of operatons: QueueVeh veh : nsert avalable vehcle nto QueueVeh QueueVeh veh : delete avalable vehcle whch has the hghest prorty from QueueVeh QueueSt p : nsert passenger nto QueueSt QueueSt p : delete passenger who has the hghest prorty from QueueSt, namely, a passenger get on one vehcle from staton III. ARCHITECTURE OF THE FLEET MANAGEMENT For the problem settng descrbed before, a dynamc fleet management system has been desgned, consstng of three parts: nformaton collecton, vehcles send and route, vehcles supervson(see Fg.2). Informaton collect from statons & vehcles vehcles assgn and route Send commands to each vehcle Runnng of each vehcle Fg. 2 System runnng flow chart Passenger s request occur vehcles supervson Refresh nformaton Of statons & vehcles The functons of the nformaton collecton part are collectng nformaton of all vehcles(such as poston and 1247

3 full load status) and requests from all statons, processng these nformaton, namely, nsert avalable vehcles nto avalable vehcles queue, nsert request from staton nto request queue. Informaton collected by data collect unt embedded n vehcles and statons can be send to central control center n a fxed tme nterval. Vehcles send and route part s a set of algorthms n central control computer whch decde the assgnment of vehcles and vehcles destned statons. Meanwhle, control commands on vehcles are also send to each vehcle. The detals of algorthms wll be gven n the next secton. The functon of vehcles supervson part s to modfy commands send by vehcles send and route part, snce some collsons may occur durng the runnng of ths transport system. Thus, collsons can be avoded. The functon of supervson part can also smplfy the desgn of send and route part. The commands receved by each vehcle are modfed commands. Informaton about vehcles and statons wll be refreshed n a fxed nterval. Three knds of commands wll be send, that are RUN, STOP, and WAIT, whch correspondng to three status of vehcles, that are runnng, stoppng and watng. The algorthm of fleet management ncludes two sectons, one s to send vehcles n the avalable vehcles queue to respond requests from watng queue n certan staton, whch correspondng to deletng the vehcle that wll be n use; the other s the nsertng new created avalable vehcle nto avalable vehcles queue. New avalable vehcle s get from passengers gettng off event happened on full loaded car. Those empty vehcles out of garage wll get RUN commands, run and enter nto garage. In ths algorthm, t seems that the garage can attract the empty vehcles whle releasng vehcles from tself when the need from statons occur. The entry of empty vehcles to the garage can avod the jam between empty vehcles and vehcles n work. Operatons of wat passengers queue n each staton are ndependent from operatons of avalable vehcles queue, the nserton operaton means the entry of new passenger n the watng queue, the deleton operaton correspond to the frst passenger gettng on a vehcle. A flow of system runnng can be descrbed as follows: 1) Refresh nformaton of QueueVeh, do QueueVeh veh Refresh nformaton of QueueSt, do QueueSt p 2) If new request appear, or new gettng on/off event happen n staton, refresh CQueueSt, do copy from QueueSt to CQueueSt (=0,1,2,3,4) 3) Do repetton from CQueueSt 4 to CQueueSt 0, produce command to each vehcle 4) Modfy commands that wll be sent accordng to status of each vehcle n vehcles supervson part 5) Each vehcle run accordng to ts command, ncludng runnng forward, pausng at staton when gettng on or off event happen(queuest p / QueueSt p ), and stoppng at garage. IV. DYNAMIC FLEET MANAGEMENT ALGORITHM The schedulng of vehcles can be descrbed as follows: for staton (>0), the vehcle watng at ths staton has the hghest prorty, the avalable vehcle that n the servce secton of staton (the secton between staton and staton -1(>0) ) has a lower prorty, the prorty lst can be traced to the garage(staton 0), and vehcles n the garage wll have the lowest prorty. In a fxed tme nterval of one flow, do repetton from staton 4 to staton 0, for a staton whose watng queue s not empty, seek avalable vehcle from avalable vehcles queue n ths staton, delete ths vehcle and send t RUN command, f there s no such vehcle or t can t meet the need from ths staton, avalable vehcle n the servce secton of ths staton wll be seek. After ths repetton, f there stll exst some statons whose watng queue s not empty, we can do lke before, begn a repetton from staton 4 to staton 0, seek avalable vehcle n statons before the staton n demand, namely, to seek vehcle whch has a lower prorty, ths repetton can be operated untl vehcles n the garage have been sought. When a vehcle s n the way to the staton n demand, f requests from statons before the scheduled staton occur, t wll respond to the staton t meets earler. Such rule wll prolong the watng tme of passengers far from the garage at the begnnng, but ths nfluence wll dspel after system has run n ts steady status, ths shortcomng can be overcome by puttng empty vehcles n the servce secton of each staton. The flow n Fg.2 run once n a tme nterval, f the length of QueueSt ( 0,1,2,3,4 ) s postve, namely, there ext requests from staton, suppose the number of wat passengers s k, then the number of vehcles we should send wll be m=k/n(n s the capacty of vehcle), f m<n, and the vehcles whch have been sent are n the way the staton n demand, because passengers gettng on or gettng off event has not happened, the length of QueueSt wll not change, accordng to the algorthm we have gven, vehcles should be sent all the same. In fact, vehcle m+1 s of no use. Ths problem can be overcome by establshng a copy queue of wat customers n each staton. The copy queue can be created by copy each staton s wat passengers queue. The refreshment of these copy queues taken only when new 1248

4 gettng on or off events occur or new passengers nsert nto certan wat queue. The establshng and use of copy queue of wat passengers can avod sendng vehcles that s not needed, thus, mnmzng the number of vehcles n workplace can be realzed. V. SIMULATION AND RESULTS The feasblty of ths dynamc fleet management algorthm was tested n a smulatve envronment. Informaton of passengers comng nto statons wth dfferent destnatons was nputted stochastcally. The travel requests can be descrbed as an orgn-destnaton matrx whch changng all along. A case of new added passengers wth ther O-D n dfferent tme s gven sequentally n table 1. TABLE 1 NEW ADDED PASSENGERS INFORMATION TABLE Tme(mn) Amount of new added passengers Orgn staton Destnaton staton Tme(mn) Amount of new added passengers Orgn staton Destnaton staton What we studed s a transportaton system n an undrectonal loop. There are 5 statons and 5 vehcles avalable (refer Fg. 3 for the crcut dagram). Each vehcle s capacty s 4 passengers. We assume that vehcles speed are equal, namely, 1.5m/s. Fg. 3 Crcut map For ths case, vehcles fleet completed ths task wth the algorthm above n 48 mnutes. Passengers average watng tme s 3.37 mnutes. In ths process, the vehcle usage rate s 4.3 vehcle per mnute, that means most of vehcles are n work n the whole work perod. Generally, the fleet management algorthm s to dspatch one vehcle at regular ntervals, just lke publc traffc company dong currently. We compared the dynamc fleet management algorthm ths paper studed wth the tradtonal dspatch method n two aspects, one aspect s the average watng tme, the other s the usage rate of vehcles. In the fxed perod dspatch algorthm, passengers arrvng events are also descrbed by table1, thus, these two algorthms can be compared n the same stuaton. The perod between two dspatches s 1.33mn, whch s close to the tme slot between two successve passenger groups arrvng event. The result of comparson s gven n table2. TABLE 2 COMPARISON OF TWO FLEET MANAGEMENT ALGORITHM Dynamc fleet management algorthm Fxed perod dspatch algorthm Average watng tme(mn) Vehcle usage rate (vehcles per mn) The result n table 2 shows clearly that the dynamc fleet management algorthm can reduce passengers watng tme as well as mprove the usage rate of vehcles. VI. CONCLUSIONS In ths paper, a new dynamc fleet management algorthm for Cybercars s ntroduced. Ths algorthm s used n an archtecture whch has been descrbed for dynamc fleet management n real tme envronment. Ths new algorthm can response passengers requests tmely. One character s ts robust for changng requests. Meanwhle, empty vehcles can collected, thus, system runnng cost can be reduced. Regardng further research, another nterestng topc s the consderaton of meshy route. In a netty stuaton, passengers journey wll be shorten, but the collson avodng and fleet coordnaton wll be more complcated. We expect that new method for ths ssue can be found n the future. REFERENCES [1] [2] Mchel Parent, Arnaud de La Fortelle. Cybercars : Past, Present and Future of the Technology INRIA, France [3] Bernhard Fleschmann, Stefan Gnutzmann, Elke Sandvoß. Dynamc Vehcle Routng Based on Onlne Traffc Informaton TRANSPORTATION SCIENCE Vol. 38, No. 4, Nov [4] Powell WB. A stochastc formulaton of the dynamc assgnment problem, wth an applcaton to truckload motor carrers. Transportaton Scence (3):

5 [5] Mattheu van der Hejden, Mark Ebben, Noud Gademann,and Aart van Harten. Schedulng vehcles n automated transportaton systems Algorthms and case study* OR Spectrum : [6] Souma Ichoua, Mchel Gendreau,Jean-Yves Potvn. Vehcle dspatchng wth tme-dependent travel tmes European Journal of Operatonal Research [7] Jan Yang, Patrck Jallet, Han Mahmassan. Real-Tme Multvehcle Truckload Pckup and Delvery Problems TRANSPORTATION SCIENCE 2004 Vol. 38, No