本文關(guān)鍵詞：基于出行模式和神經(jīng)網(wǎng)絡(luò)的地鐵短時客流預(yù)測方法研究　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 時間序列 短時客流預(yù)測 出行模式 螢火蟲算法 Elman神經(jīng)網(wǎng)絡(luò)

【摘要】：建設(shè)軌道交通系統(tǒng)是緩解交通壓力的有效途徑之一,對地鐵短時客流的準(zhǔn)確預(yù)測可以為地鐵車次的智能調(diào)度、站點限流與客流疏散方案的制定提供依據(jù)。本文針對短時客流具有非線性、時變性的特點,選擇神經(jīng)網(wǎng)絡(luò)作為預(yù)測模型,并提出將通勤因素與短時客流預(yù)測結(jié)合。由于神經(jīng)網(wǎng)絡(luò)的性能很大程度上依賴于模型初始參數(shù)的設(shè)置,因此本文提出一種改進的螢火蟲算法用于優(yōu)化神經(jīng)網(wǎng)絡(luò)的初始參數(shù)。本文的主要工作如下:(1)基于上海市地鐵一卡通數(shù)據(jù)對地鐵客流進行特征分析:針對周內(nèi)客流特征存在差異,使用層次聚類法對客流聚類,并借鑒上海市綜合交通年度報告分析周五及長假前一日客流的特征,對聚類結(jié)果進一步細化;計算待預(yù)測時間片客流量和歷史客流序列之間的Spearman相關(guān)系數(shù);根據(jù)乘客出行鏈的相關(guān)理論設(shè)計了10種出行模式,在此基礎(chǔ)上提出本文對“通勤”的定義,利用Hadoop平臺編程計算各站點在各時間片內(nèi)的通勤乘次,并說明本文從一卡通數(shù)據(jù)中分離出的通勤客流在一段時間內(nèi)具有時空穩(wěn)定性。(2)基于上海市氣象局、環(huán)保局提供的相關(guān)數(shù)據(jù)分析降雨量和空氣質(zhì)量指數(shù)對短時客流的影響。(3)將時間相關(guān)性較高的客流序列作為輸入,比較BP神經(jīng)網(wǎng)絡(luò)和Elman神經(jīng)網(wǎng)絡(luò)的預(yù)測性能,進一步縮小客流序列的最優(yōu)輸入維數(shù)所處區(qū)間,并選定更適應(yīng)時變性的Elman神經(jīng)網(wǎng)絡(luò)作為預(yù)測模型;將通勤、天氣因素與短時客流預(yù)測結(jié)合,驗證本文提出的通勤因素能大幅提高預(yù)測精度,并選定性能最好的輸入組合作為預(yù)測模型的輸入。(4)介紹元啟發(fā)式優(yōu)化算法,詳細分析了螢火蟲算法(FA)的原理、流程和優(yōu)缺點,并針對其存在的缺點提出改進:引入混沌機制和“鯰魚效應(yīng)”提高算法的全局搜索能力;引入Levy飛行提高算法的局部探索能力;對每只螢火蟲個體采用自適應(yīng)步長策略以提高算法的尋優(yōu)精度。通過比較不同優(yōu)化算法的收斂速度和尋優(yōu)精度,驗證了改進的FA算法的有效性;通過比較不同模型的預(yù)測性能,驗證了基于改進的FA算法優(yōu)化后的預(yù)測模型的有效性。
[Abstract]:The construction of rail transit system is one of the effective ways to relieve traffic pressure. The accurate prediction of subway short-term passenger flow can be used for the intelligent dispatching of subway train. In view of the nonlinear and time-varying characteristics of short-term passenger flow, the neural network is selected as the prediction model. It is proposed that the commuting factor be combined with the short-term passenger flow prediction, because the performance of the neural network depends on the setting of the initial parameters of the model to a great extent. Therefore, an improved firefly algorithm is proposed to optimize the initial parameters of neural network. The main work of this paper is as follows: 1). Based on the data of Shanghai Metro Card, this paper analyzes the characteristics of subway passenger flow: there are differences in the characteristics of the passenger flow during the week. The hierarchical clustering method is used to cluster the passenger flow, and the characteristics of the passenger flow on Friday and 1st before the long holiday are analyzed by using the annual comprehensive traffic report of Shanghai, and the clustering results are further refined. The Spearman correlation coefficient between the passenger flow and the historical passenger flow sequence was calculated. According to the related theory of passenger travel chain, 10 travel modes are designed. On the basis of this, the definition of "commuting" is put forward in this paper, and the commuting times of each station in each time slice are calculated by programming with Hadoop platform. It also shows that the commuter flow separated from the card data in this paper has temporal and spatial stability for a period of time.) based on the Shanghai Meteorological Bureau. Relevant data provided by EPA to analyze the effect of rainfall and air quality index on short-term passenger flow. The prediction performance of BP neural network and Elman neural network is compared to further reduce the interval of optimal input dimension of passenger flow sequence. Elman neural network, which is more suitable for time-varying, is chosen as the prediction model. By combining commuting, weather factors and short-term passenger flow forecasting, it is verified that the commuting factors proposed in this paper can greatly improve the prediction accuracy. The best performance input combination is selected as the input. 4) the heuristic optimization algorithm is introduced, and the principle, flow chart, advantages and disadvantages of the firefly algorithm are analyzed in detail. In view of its shortcomings, some improvements are put forward: the introduction of chaos mechanism and the "catfish effect" to improve the global search ability of the algorithm; Levy flight is introduced to improve the local exploration ability of the algorithm. For each individual, adaptive step size strategy is used to improve the accuracy of the algorithm. The effectiveness of the improved FA algorithm is verified by comparing the convergence speed and the optimization accuracy of different optimization algorithms. The effectiveness of the optimized prediction model based on the improved FA algorithm is verified by comparing the prediction performance of different models.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U293.13;TP183

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