本文關鍵詞：基于機器學習方法的城市對外客運交通需求預測研究　出處：《哈爾濱工業(yè)大學》2015年碩士論文　論文類型：學位論文

  更多相關文章： 對外客運需求 機器學習 降噪自編碼 隨機森林

【摘要】：城市對外客運交通需求預測是城市開展城市綜合交通系統(tǒng)規(guī)劃與設計的基礎工作,合理準確的交通需求預測可為城市的對外客運樞紐系統(tǒng)選址、布局、方案比選等工作提供數(shù)據(jù)支撐,實現(xiàn)既滿足城市居民出行需求,又節(jié)約項目建設資金的目標。由于對外客運需求預測研究中相關影響因素之間存在日趨增加的相關性關系以及統(tǒng)計數(shù)據(jù)中的異常值等原因,傳統(tǒng)的時間慣性與相關因素原理預測模型表現(xiàn)欠佳。近幾年由于社會統(tǒng)計工作的逐漸完善,可供選擇研究統(tǒng)計數(shù)據(jù)不斷積累增多,為學者使用新型方法進行研究提供了相關基礎。本文采用機器學習中降噪自編碼、隨機森林兩種方法進行交通需求預測,以緩解淺層機器學習方法在交通需求預測問題中的不足。首先引入深度學習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測網(wǎng)絡初始化參數(shù),使得網(wǎng)絡初始總體損失值較優(yōu),緩解了淺層需求預測方法的局部極值與梯度彌散問題。此外人工主動隨機噪聲,迫使網(wǎng)絡在輸入包含噪聲的情況下重構原始輸入,進而訓練所得交通需求預測網(wǎng)絡魯棒性、泛化能力更強,不易過擬合。另外考慮對外客運出行需求的相關影響因素間的關聯(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器學習中的隨機森林方法相結(jié)合,提出時間窗-隨機森林組合方法的對外客運總體需求預測方法。隨機森林方法在訓練過程中共進行兩重隨機過程,第一重隨機為在宏觀交通相關數(shù)據(jù)總體訓練樣本中隨機抽取部分樣本訓練決策樹模型,未被抽取數(shù)據(jù)用以評價所得交通需求決策樹預測模型泛化性能,多次隨機抽樣獲得多顆決策樹構成交通需求預測森林模型;第二重隨機為在單棵決策樹節(jié)點分裂過程中隨機選取部分屬性。兩重隨機過程使得模型過度擬合特定樣本的概率大大減少,預測模型的泛化性增強。同時以北京市宏觀經(jīng)濟影響因素數(shù)據(jù)集為基礎進行實例分析,模型精度良好,驗證了方法的可行性和有效性,可運用于對外客運需求預測工作。本研究側(cè)重基于機器學習方法的對外客運需求預測,分別從方法由來、數(shù)學原理與方法實現(xiàn)等方面進行了詳細闡述,可對省份、城市等范圍區(qū)域進行交通運輸發(fā)展規(guī)劃研究工作提供參考與借鑒。對機器學習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.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U12

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