本文關(guān)鍵詞：基于機(jī)器學(xué)習(xí)方法的城市對外客運(yùn)交通需求預(yù)測研究　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 對外客運(yùn)需求 機(jī)器學(xué)習(xí) 降噪自編碼 隨機(jī)森林

【摘要】：城市對外客運(yùn)交通需求預(yù)測是城市開展城市綜合交通系統(tǒng)規(guī)劃與設(shè)計(jì)的基礎(chǔ)工作,合理準(zhǔn)確的交通需求預(yù)測可為城市的對外客運(yùn)樞紐系統(tǒng)選址、布局、方案比選等工作提供數(shù)據(jù)支撐,實(shí)現(xiàn)既滿足城市居民出行需求,又節(jié)約項(xiàng)目建設(shè)資金的目標(biāo)。由于對外客運(yùn)需求預(yù)測研究中相關(guān)影響因素之間存在日趨增加的相關(guān)性關(guān)系以及統(tǒng)計(jì)數(shù)據(jù)中的異常值等原因,傳統(tǒng)的時間慣性與相關(guān)因素原理預(yù)測模型表現(xiàn)欠佳。近幾年由于社會統(tǒng)計(jì)工作的逐漸完善,可供選擇研究統(tǒng)計(jì)數(shù)據(jù)不斷積累增多,為學(xué)者使用新型方法進(jìn)行研究提供了相關(guān)基礎(chǔ)。本文采用機(jī)器學(xué)習(xí)中降噪自編碼、隨機(jī)森林兩種方法進(jìn)行交通需求預(yù)測,以緩解淺層機(jī)器學(xué)習(xí)方法在交通需求預(yù)測問題中的不足。首先引入深度學(xué)習(xí)理論中降噪自編碼方法:降噪自編碼方法通過數(shù)據(jù)的逐層自編碼、解碼過程獲得良好的交通需求預(yù)測網(wǎng)絡(luò)初始化參數(shù),使得網(wǎng)絡(luò)初始總體損失值較優(yōu),緩解了淺層需求預(yù)測方法的局部極值與梯度彌散問題。此外人工主動隨機(jī)噪聲,迫使網(wǎng)絡(luò)在輸入包含噪聲的情況下重構(gòu)原始輸入,進(jìn)而訓(xùn)練所得交通需求預(yù)測網(wǎng)絡(luò)魯棒性、泛化能力更強(qiáng),不易過擬合。另外考慮對外客運(yùn)出行需求的相關(guān)影響因素間的關(guān)聯(lián)性和時間慣性,將時間序列數(shù)據(jù)研究中的窗口滑移與機(jī)器學(xué)習(xí)中的隨機(jī)森林方法相結(jié)合,提出時間窗-隨機(jī)森林組合方法的對外客運(yùn)總體需求預(yù)測方法。隨機(jī)森林方法在訓(xùn)練過程中共進(jìn)行兩重隨機(jī)過程,第一重隨機(jī)為在宏觀交通相關(guān)數(shù)據(jù)總體訓(xùn)練樣本中隨機(jī)抽取部分樣本訓(xùn)練決策樹模型,未被抽取數(shù)據(jù)用以評價(jià)所得交通需求決策樹預(yù)測模型泛化性能,多次隨機(jī)抽樣獲得多顆決策樹構(gòu)成交通需求預(yù)測森林模型;第二重隨機(jī)為在單棵決策樹節(jié)點(diǎn)分裂過程中隨機(jī)選取部分屬性。兩重隨機(jī)過程使得模型過度擬合特定樣本的概率大大減少,預(yù)測模型的泛化性增強(qiáng)。同時以北京市宏觀經(jīng)濟(jì)影響因素?cái)?shù)據(jù)集為基礎(chǔ)進(jìn)行實(shí)例分析,模型精度良好,驗(yàn)證了方法的可行性和有效性,可運(yùn)用于對外客運(yùn)需求預(yù)測工作。本研究側(cè)重基于機(jī)器學(xué)習(xí)方法的對外客運(yùn)需求預(yù)測,分別從方法由來、數(shù)學(xué)原理與方法實(shí)現(xiàn)等方面進(jìn)行了詳細(xì)闡述,可對省份、城市等范圍區(qū)域進(jìn)行交通運(yùn)輸發(fā)展規(guī)劃研究工作提供參考與借鑒。對機(jī)器學(xué)習(xí)理論與交通問題的結(jié)合有著積極的作用。
[Abstract]:Urban external passenger transport demand prediction is the basic work of urban comprehensive transportation system planning and design. Reasonable and accurate traffic demand prediction can be used for the location and layout of urban external passenger transport hub system. The scheme provides data support to meet the travel needs of urban residents. The goal of saving project construction funds. Due to the increasing correlation between the related factors and the abnormal value in the statistical data in the forecast study of passenger demand for foreign passenger transport, and so on. The traditional prediction model of time inertia and related factors is not good. In recent years, due to the gradual improvement of social statistics, it is possible to choose to study the statistical data accumulation and increase. In this paper, two methods of noise reduction in machine learning and stochastic forest are used to forecast traffic demand. In order to alleviate the deficiency of shallow machine learning method in traffic demand prediction problem. Firstly, the noise reduction self-coding method is introduced in depth learning theory: noise reduction self-coding method through the data layer by layer self-coding. In the decoding process, good traffic demand prediction network initialization parameters are obtained, which makes the initial total loss value of the network better. The problem of local extremum and gradient dispersion of shallow demand prediction method is alleviated. In addition, artificial active random noise forces the network to reconstruct the original input when the input contains noise. Furthermore, the trained traffic demand forecasting network is robust, more generalized and difficult to be over-fitted. In addition, the correlation and time inertia among the related factors of external passenger travel demand are considered. The window slippage in time series data is combined with the stochastic forest method in machine learning. A time window-stochastic forest combination method is proposed to predict the total demand of passenger transport. The stochastic forest method carries out double stochastic processes during the training process. The first is random training decision tree model which is randomly selected from the total training samples of macro-traffic related data, and is not extracted to evaluate the generalization performance of the traffic demand decision tree prediction model. Multiple random sampling to obtain multiple decision trees to form a forest model of traffic demand prediction; The second random is the random selection of some attributes in the split process of a single decision tree node. The probability of overfitting a particular sample is greatly reduced by the double random process. The generalization of the prediction model is enhanced. At the same time, based on the data set of the macroeconomic impact factors in Beijing, the model has good accuracy, which verifies the feasibility and effectiveness of the method. This research focuses on forecasting the demand of foreign passenger transport based on machine learning method, respectively from the origin of the method, mathematical principles and the realization of the method are described in detail. It can be used as a reference for the study of transportation development planning in provinces, cities and other areas, and has a positive effect on the combination of machine learning theory and traffic problems.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U12

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