【正文】 提出的模糊系統(tǒng)允許連續(xù)停車要求的檢測和模糊規(guī)則庫的間斷更新。拒絕或接受司機(jī)停車的數(shù)量應(yīng)該作為顧客滿意程度的尺度。 正如我們先前指出，任何停車定價策略的主要作用是在一定的時間階段內(nèi)減少車輛旅行的總數(shù)量。這的確是一個基本的公共領(lǐng)域的目標(biāo)。 然而，在我們的模型中，我們集中精力在停車管理收入的最大化，那個一個私營部分的目標(biāo)。這倆個目標(biāo)可能看起來是不一致的，人們會質(zhì)問后者會怎么達(dá)到前者所要減少車輛旅行的目標(biāo)。 市場細(xì)分是這樣一種情況，不同的司機(jī)愿意為相同的東西付不同的價錢。我們先前已經(jīng)說明了一個倆種類型的司機(jī)，商人和一個靠養(yǎng)老金生活的老人 — 生意人愿意在開會十五分鐘前停車付更高的停車費，而一個靠養(yǎng)老金生活的老人為了更低的停車費，要和妻子徒步穿越市中心，提前一天預(yù)訂車位。然而，即時最富有的商人也不愿意為短暫的停車位支付上億美元。只是，停車需求的數(shù) 量刻畫了不同群體的司機(jī)，市場細(xì)分根據(jù)這種行為得到實施（定義 2,3 或者 10 種不同類型的司機(jī)或者停車位），同樣針對不同的停車類型有不同的停車費。在極端的情況下，不是根據(jù)市場細(xì)分模式和停車費免費提供，理論上只有最富有的人才會開車。在這種情況下，即時在一段時間內(nèi)車輛旅行的總數(shù)量會減少，但是社會的抱怨肯定會到達(dá)某種程度。因此，收入最大化可以通過給特定的司機(jī)和停車類型留一定的停車位數(shù)量取得。通過這種指定限制的介紹，保護(hù)老人，殘疾人和其它層次司機(jī)的停車位也是可行的。 司機(jī)是否會接受停車預(yù)定系統(tǒng)和稅收管理系統(tǒng)是未知的。進(jìn)一 步的研究將會檢查智能停車系統(tǒng)能否減少停車場前的隊伍，車輛旅行的總數(shù)量，平均旅行時間，能源消耗，還有空氣污染。 致謝 在此感謝那些匿名的評論和建議，大大提高了這篇文章。 參考文獻(xiàn) [1]Arnott,R.,Rowse,J., of Urban Economics 45,97–124. [2]Axhausen,.,Polak,J., of parking:Stated preference 18,59–81. [3]Belobaba,., yield management airline yield management:An overview of seat inventory Science 21,66–73. [4]Brumelle,.,McGill,., seat allocation with multiple nested fare Research 41,127–137. [5]Cross,R., Books,New York. [6]Littlewood,K., and control of passengers :Proceedings of the 12th AGIFORS –117. [7]Polak,J.,Axhausen,., search behavior:A review of current research and future Studies Units,Working Paper 540,Oxford University. [8]Saltzman,., animated simulation model for analyzing onstreet parking 69,79–90. [9]Shoup,., trouble with minimum parking Research 33A,549–574. [10]Steiner,., generation and parking requirements in traditional shopping Research Records 1617,28–37. [11]Tam,.,Lam,., car ownership under constraints of road capacity and parking Research 34A,145–170. [12]Teodorovi′c,D.,Vukadinovi′c,K.,?c Control and Transport Planning:A Fuzzy Sets and Neural Networks Approach. Kluwer Academic Publishers,Boston/Dordrecht/London. [13]Teodorovi′c,D.,Popovi′c,J.,Pavkovi′c,G.,Kikuchi,S., airline seat inventory control Planning and Technology 25,155–173. [14]Thompson,.,Richardson,., parking search Research 33A,159–170. [15]Van der Goot,D., model to describe the choice of parking Research 16A,109–155. [16]Verhoef,E.,Nijkamp,P.,Rietveld,P., economics of regulatory parking policies:The(im)possibilities of parking policies in traffc Research 29A,141–,W., theory and transport Economic Review 59,251–260. [17]Vickrey,W., to the Joint Committee on Washington DC,Metropolitan of Urban Economics 36,42–65. [18]Wang,.,Mendel,., fuzzy rules by learning from Transactions on Systems,Man and Cyberics 22,1414–1427. [19]Waterson,.,Hounsell,.,Chatterjee,K., the potential savings in travel time resulting from parking guidance systems—a simulation case of the Operational Research Society 10,1067–1077. [20]Wong,.,Tong,.,Lam,.,Fung,., of parking demand models in Hong of Urban Planning and development—ASCE 126,55–74. [21]Young,W.,Taylor,.,Thompson,.,Ker,I.,Foster,J.,:A software package for the evaluation of parking systems in central Engineering and Control 32,186–193. [22]Young,W.,Thompson,.,Taylor,., review of urban parking Reviews 11,63–84. [23]′,′/European Journal of Operational Research 175(2021)1666–1681 1681 附件 2：外文原文 （復(fù)印件） Intelligent parking systems Abstract The basic concepts of the parking reservation system and parking revenue management system are discussed in this proposed＂intelligent＂ parking space inventory control system that is based on a bination of fuzzy logic and integer programming techniques makes＂on line＂ decisions whether to accept or reject a new drivers request for the first step of the proposed model,the best parking strategies are developed for many dfferent patterns of vehicle parking strategies are developed using integer programming the second step,learning from the best strategies,specific rules are uniqueness of the proposed approach is that the rules are derived from the set of chosen examples assuming that the future traffc arrival patterns are results were found to be close to the best solution assuming that the future arrival pattern is known. Keywords:Traffc。Uncertainty modeling。Control。Parking。Fuzzy logic 1. Introduction Every day a significant percentage of drivers in singleoccupancy vehicles search for a parking space. Additionally,less experienced drivers or outoftowners further contribute to the increase of traffc for a vacant parking space is a typical example of a search parking search strategy is posed of a set of vague is usually diffcult to describe these rules type of the planned activity,time of a day,day of the week,current congestion on particular routes,knowledge of city streets,and potentially available parking places have significant influence on a chosen parking search the last four decades numerous parking search models have been developed .In many decisionmaking situations in transportation（ modal split,choice of air carrier,choice of airport,etc.） the petitive alternatives and their characteristics are reasonably well known in advance to the decision maker(passenger,driver).On the other hand,the driver`s usually discover different parking alternatives one by one in a temporal ,this temporal sequence has a very strong influence on the drivers final decision about the parking place. During,the,past,two,decades,traffc,authorities,in,many,cities,Helsinki,Cologne,Mainz,Stuttgart,Wiesbaden,Aalb,Hague have started to inform and guide drivers to parking facilities with realtime variable message signs[directional arrows,names of the parking facilities,status（ full,not full,number of available parking spaces,etc.） ]. Information about the number of available parking spaces could be displayed on the major roads,streets and intersections,or it could be distributed through the Inter. It is logical to ask the quest