本文關(guān)鍵詞： 泊位預(yù)測 誘導停車 神經(jīng)網(wǎng)絡(luò) 多屬性決策 路徑尋優(yōu)　出處：《哈爾濱商業(yè)大學》2015年碩士論文　論文類型：學位論文

【摘要】：隨著城市居民私家車的擁有量急劇上升,停車難的問題日益突出。停車誘導系統(tǒng)主要解決停車場車位與駕駛員駕駛的車輛之間的供求關(guān)系平衡,但是現(xiàn)有的停車誘導技術(shù)主要注重停車場外部的區(qū)域引導,對于停車場內(nèi)部的引導機制單一,一般僅僅隨機給出一條從停車場入口處到有效空余泊位的最短路徑,并沒有從駕駛員所考慮的停車因素角度來進行誘導。此外對于停車場外部的引導,如果加入停車場的空余泊位預(yù)測技術(shù),則會有效的幫助場外誘導系統(tǒng)。 本文首先分析了目前主要的時間序列預(yù)測技術(shù),在此基礎(chǔ)之上,提出了利用BP神經(jīng)網(wǎng)絡(luò)算法來進行停車場空余泊位的預(yù)測。對標準的BP神經(jīng)網(wǎng)絡(luò)的原理進行了詳細的研究,針對停車場空余泊位預(yù)測的問題進行BP算法的網(wǎng)絡(luò)結(jié)構(gòu)確定,并且針對BP神經(jīng)網(wǎng)絡(luò)訓練過程中易震蕩、收斂速度過慢和容易陷入局部最小的缺點,采用BP動量法與調(diào)節(jié)學習速率相結(jié)合的方法對其進行改進。在進行實驗數(shù)據(jù)仿真時,通過數(shù)據(jù)預(yù)處理技術(shù)對停車場空余泊位數(shù)據(jù)進行了變換,防止了因數(shù)據(jù)過大而造成的網(wǎng)絡(luò)癱瘓。通過仿真驗證了BP神經(jīng)網(wǎng)絡(luò)對停車場空余泊位數(shù)預(yù)測的有效性,為停車場場外誘導提供了幫助。 對于停車場場內(nèi)部分,本文利用多屬性決策的方法進行路徑尋優(yōu)來實現(xiàn)場內(nèi)誘導。在分析駕駛員選擇車位的主要考慮因素的基礎(chǔ)上,利用Dijkstra算法確定行駛距離與路徑、利用歐幾里得距離確定步行距離以及利用三角模糊數(shù)期望值的方法確定停車位的環(huán)境信息值,來確定決策屬性矩陣,最終利用基于灰熵關(guān)聯(lián)度多屬性決策的方法進行了停車場有效空余泊位的屬性排序。其排序中最優(yōu)的屬性所對應(yīng)的空余泊位即是最優(yōu)泊位,所對應(yīng)的行駛路徑即為最優(yōu)路徑。
[Abstract]:With the rapid increase in the number of private cars owned by urban residents, the problem of parking difficulties is becoming increasingly prominent. The parking guidance system mainly solves the balance of supply and demand between parking spaces and vehicles driven by drivers. However, the existing parking guidance technology mainly pays attention to the regional guidance outside the parking lot. For the single guiding mechanism within the parking lot, the shortest path from the entrance of the parking lot to the effective free berth is generally given at random. In addition, for the outside guidance of the parking lot, if the free berth prediction technology is added, it will help the off-site guidance system effectively. Based on the analysis of the main time series prediction techniques, a BP neural network algorithm is proposed to predict the parking space. The principle of the standard BP neural network is studied in detail. The network structure of BP algorithm is determined for the prediction of parking space, and the shortcomings of BP neural network are that it is easy to concussion, converge too slowly and fall into local minimum easily in the training process of BP neural network. The BP momentum method and the method of adjusting the learning rate are adopted to improve it. In the simulation of the experimental data, the data of the parking lot free berth are transformed by the data preprocessing technology. The simulation results show that the BP neural network is effective in predicting the number of parking spaces, which is helpful for the off-site induction of parking lots. For the part of parking yard, this paper uses the method of multi-attribute decision to optimize the route to realize the induction in the field. On the basis of analyzing the main factors of driver's choice of parking space, the Dijkstra algorithm is used to determine the driving distance and path. The Euclidean distance is used to determine the walking distance and the triangular fuzzy number expectation value is used to determine the environmental information value of the parking space to determine the decision attribute matrix. Finally, the method of grey entropy correlation multi-attribute decision-making is used to sort the attributes of the parking lot's effective free berth. The optimal berth corresponding to the optimal attribute is the optimal berth, and the corresponding driving path is the optimal path.
【學位授予單位】：哈爾濱商業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.7;U495

【引證文獻】

相關(guān)碩士學位論文 前1條

1 王盛莉;基于私家泊位共享的智能停車選擇研究[D];吉林大學;2016年

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本文編號：1521038