【正文】 3) 三個城市中心區(qū)從不同角度對停車場模型進行了探討，對一些共同問題提出了多種有創(chuàng)意的解決辦法，尤其是針對駕駛人尋找停車位造成的交通擁堵問題。不過，受最近經(jīng)濟滑坡的影響，該地區(qū)部分業(yè)主已停止營業(yè)或是將物業(yè)出售，相關機構也推遲了規(guī)劃、評估工作，但在經(jīng)濟復蘇時該體系即可使用。盡管如此，仿真結果依然表明在新維根市投資建設停車誘導系統(tǒng)的方案是合理的，可以充分利用停車場的容量。按照駕駛人對出行目的地周邊停車場的熟悉度對矩陣做進一步細化。一旦有車輛從該停車場離開，則會重新回到名單中供待停車輛選擇。根據(jù)城市停車管理系統(tǒng) (the Town Parking Manager)的經(jīng)驗記錄， ITS 系統(tǒng)的設置是僅 20%的駕駛人遵循停車誘導系統(tǒng)的建議，其余 80%的駕駛人將駛向自己的首選停車場，若該停車場已滿，再改變方向駛向其他停車場。若此過程中發(fā)現(xiàn)某車輛的出發(fā)時間與預計出發(fā)時間的誤差在20%以內(nèi)，則該車輛將被添加到出發(fā)時段分布圖中。傳統(tǒng)的 OD矩陣法不能滿足此要求，需要使用更復雜的控制方法來確定車輛的駛離時間和地點。細化程度取決于已有調(diào)查數(shù)據(jù)，同時，停車場分類的詳細程度應與模型輸入數(shù)據(jù)相適應。若需要對模型做進一步改進，可通過在需求矩陣中同時包含接送車輛的往返方向實現(xiàn)。羅克代爾市的交通需求矩陣數(shù)據(jù)主要來自路邊訪問，顯示了出行的真實目的。通過調(diào)整不同停車場的入口費用或停車許可證的許可水平可以測試駕駛人對停車條件變化的反應。建模人員可據(jù)此區(qū)分接受較長步行距離和較高費用的駕駛人。另一項在英格蘭羅克代爾市(Rockdale)的研究 (見圖 1)仿真了停車位分布與城市中心區(qū)發(fā)展規(guī)劃的關系，其目標是優(yōu)化停車設施與其周邊土地利用的關系；在設計過程的早期階段，通過改變停車場車位的供應情況來控制城市中心區(qū)的交通擁堵狀況。 《英國道路設計指南》對停車場的可達性做了說明。 1927 年，底特律 (Detroit)市有兩個區(qū)做了類似調(diào)查，相關數(shù)據(jù)分別為 19% 和 34%[2]。 關鍵詞 ： 交通模型；微觀仿真；矩陣細化；停車場 ； 規(guī)劃 2 對市區(qū)駕駛人來說，停車設施供給是最具爭議的問題之一。描述了三個城市采用的不同研究方法及得到的研究結果。 駕駛人在道路上循環(huán)駕駛尋找停車位，可能是城市中心區(qū)擁堵的主要原因。積極的管理政策正在 被廣泛采用，以限制停車位數(shù)量、鼓勵可持續(xù)的交通方式 [5]。目前缺乏的3 環(huán)節(jié)是，在城市早期規(guī)劃階段交通規(guī)劃政策對停車場設施及其可達性的影響研究。 車輛到達 停車場在微觀仿真模型中是一個整體，與目的地小區(qū)相連，且一個停車場可服務多個小區(qū)。一個停車場可能有多個相鄰的入口，為讓車輛支付適當?shù)耐＼囐M用，每個入口都有相應的限制條件。 2 仿真對策 數(shù)據(jù)收集 新維根市交通需求矩陣來自于已有宏觀模型，并使用調(diào)查數(shù)據(jù)進行調(diào)整。數(shù)據(jù)清單包含市中心和周邊的所有停車場，同時還包括鄰近市中心的居民停車區(qū)。在塔卡普納區(qū)和新 維根市，收集的停車場數(shù)據(jù)包括車輛到達時間、停留時間和停車場占有率。依據(jù)工作場所是否有停車位，將長時間停車進一步分為工作場所就地停車 (on site)或公共停車場停車兩類。 塔卡普納區(qū)每個分區(qū)包含有車輛出發(fā)時間分布情況的出行需求量， OD 矩陣即基于此生成。 8 停車場的尋找 上述三個模型的研究對象均為停車場對城市交通擁堵的影響，因此，研究成功的關鍵是模型把車輛分配到停車場的策略以及車輛尋找停車場的過程。塔卡普納區(qū)模型通過外接軟件控制器管理停車場車位，同時對車輛尋找停車場的條件進行限定 (尋找界限 )，從而對車輛選擇停車場的過程進行補充。 針對這一問題，塔卡普納區(qū)提出了兩種解決方法： 1)使用停車場車位使用情況控制器覆蓋駕駛人的選擇，為駕駛人提供替代停車場。這一效果是在 20%的駕駛人遵循停車誘導系統(tǒng)的情況下實現(xiàn)的。 塔卡普納區(qū) 11 塔卡普納區(qū) SParamics 模型是塔卡普納中心區(qū)所有重要規(guī)劃申請和地區(qū)性規(guī)劃變更的交通運行評估工具，以保證所有評估在同一仿真體系下完成。三個模型中駕駛 人選擇停車場的方法也有差異，但都是以矩陣細化為基礎。 public or private, and charged or free. This included all car parks within, or adjacent to, the town centre. Residential parking areas adjacent to the town centre, were also included as these provided free parking, with longer walk distances, and were often used by muters. Areas with high dropoff trips were modelled as a private parking type at their destination but could have been improved by having both the inbound and outbound legs of the dropoff trip in the matrix. The car park data also provided charging information for each car park. When bined with the car park interview data it was found to be possible to group charges into a single short stay and long stay charge. This simplification was appropriate in Rochdale, but it would have been possible to use a car park specific charge if the variation was more 23 significant. Parking data in Rochdale was limited to peak car park occupancy and the model would have benefited from a prehensive survey of vehicles entering and leaving throughout the day. In Takapuna, and Nieuwegein, arrival time, dwell time, and occupancy data was available and this was used, with the charging information, to model driver?s choice of car park. Demand matrix segmentation Matrix segmentation enables the modeller to control departure time demands for different classes of vehicles. The degree of segmentation must be supported by the data. Similarly car parks should be grouped to a level mensurate with the detail of model input data. In Rochdale, matrices were derived for cars categorized by muter, nonmuter and work. The interview data enabled the muter and work matrices to be subdivided into private non residential (PNR) parking and contract parking. PNR parking supply, which is is notoriously difficult to estimate, was assumed to be unlimited in the model as the matrixes were explicity defined. For areas outside the town centre all drivers were assumed to park at their destinations. Takapuna adopted a similar approach, with demands segmented into ?long stay? and ?short stay? parking stay was further split into ?on site? or ?general? depending on access to workplace car parking. Long and short stay demands were estimated from the purpose matrices of a 24 strategic transport model, with adjustments based on car park number plate and turn count data. Trip linking In order to model vehicles arriving and leaving from the same car park, trips in and out of the city centre area must be linked, with origin car parks and departure times selected based on prior arrival car parks and times. This requires more sophisticated control over the departure time and the departure location than is available from conventional OD matrix methodologies. In Nieuwegein, the study period covered the main shopping peak period on Saturday afternoon and included a warm up period to populate the car parks and initialise the ITS controller within the microsimulation model. To model the linked trips, all traffic departing from the city centre was deleted from the OD matrices. An external software controller monitored the car park occupancy in the model to determine the arrival profile and, after a suitable dwell time typically one hour, released matching vehicles on return trips. For Takapuna, before the PM simulation was run, a separate demand model was used to generate a profile of releases in the PM peak based on car park occupancy derived from the AM peak. This was to be subsequently used in the PM model run. Matrices were generated based on the profiled demands derived for 25 each zone within Takapuna. When selecting from which particular car park the vehicle should depart, the demand model matched its outbound zone with that of a parked vehicle. The match was made based on the parking duration (long/short), and the expected departure time estimated from the arrival time distribution. If this process found a vehicle within 20% of its expected departure time, then one was added