本文選題：多元線性回歸模型 + 隨機(jī)森林回歸模型　； 參考：《大連海事大學(xué)》2017年碩士論文

【摘要】：由于社會(huì)經(jīng)濟(jì)的快速發(fā)展,出租車逐漸成為了人們出行的首選方式。近年來(lái)隨著網(wǎng)絡(luò)技術(shù)的發(fā)展和智能終端的普及,網(wǎng)絡(luò)打車平臺(tái)下的出租車(以下簡(jiǎn)稱出租車)憑借其快速、便捷和優(yōu)質(zhì)的服務(wù)逐漸的成為人們出行的首選。然而,在現(xiàn)實(shí)中出租車司機(jī)很難知道城市中不同時(shí)刻不同區(qū)域的出租車需求量,這樣可能會(huì)導(dǎo)致出租車出現(xiàn)空載或者供不應(yīng)求的現(xiàn)象,極大的浪費(fèi)了社會(huì)資源。為了解決這一問題本文研究出租車需求量預(yù)測(cè)模型,對(duì)未來(lái)不同時(shí)刻的出租車需求量進(jìn)行預(yù)測(cè)。本文首先研究了出租車需求量預(yù)測(cè)問題的常用研究方法以及相關(guān)的短時(shí)交通流預(yù)測(cè)模型,對(duì)常用模型進(jìn)行了分析指出了各自的特點(diǎn)。通過使用數(shù)據(jù)可視化的方式驗(yàn)證了天氣狀況、PM2.5、溫度和交通擁堵狀況等因素對(duì)出租車的短時(shí)需求具有一定的影響,然后借鑒短時(shí)交通流預(yù)測(cè)問題構(gòu)造變量的方式對(duì)樣本的變量進(jìn)行了提取和設(shè)計(jì),為模型的建立奠定了基礎(chǔ)。接著本文構(gòu)建了多元線性回歸模型、隨機(jī)森林回歸模型、梯度漸進(jìn)回歸樹模型,并基于這三個(gè)模型提出了一個(gè)線性變權(quán)重組合預(yù)測(cè)模型來(lái)預(yù)測(cè)出租車的需求量。其中組合預(yù)測(cè)模型的權(quán)重是根據(jù)單一模型各自的歷史預(yù)測(cè)誤差的均方倒數(shù)來(lái)確定的,并且這個(gè)模型的權(quán)重是實(shí)時(shí)調(diào)整的。最后本文以中國(guó)某一線城市出租車需求量最大的區(qū)域的數(shù)據(jù)為例,驗(yàn)證本文構(gòu)建的組合預(yù)測(cè)模型的有效性,實(shí)驗(yàn)表明線性變權(quán)重的組合預(yù)測(cè)模型的整體預(yù)測(cè)精度要高于單一預(yù)測(cè)模型的預(yù)測(cè)精度。為了保證模型的通用性和便捷性,本文利用Rserve實(shí)現(xiàn)了 JAVA語(yǔ)言與R語(yǔ)言交互的出租車需求量預(yù)測(cè)系統(tǒng),考慮到系統(tǒng)的響應(yīng)時(shí)間,該系統(tǒng)采用C/S架構(gòu)。
[Abstract]:With the rapid development of social economy, taxi has gradually become the preferred way for people to travel. In recent years, with the development of network technology and the popularization of intelligent terminals, taxi (taxi) under the network taxi platform has gradually become the first choice for people to travel by virtue of its fast, convenient and high quality service. However, in reality, it is difficult for taxi drivers to know the demand for taxis in different areas at different times in the city, which may lead to the phenomenon of no load or short supply of taxis, which is a great waste of social resources. In order to solve this problem, this paper studies the forecast model of taxi demand and forecasts taxi demand at different times in the future. This paper first studies the common research methods of taxi demand forecasting and the related short-term traffic flow forecasting models, and points out the characteristics of the common models. Through the use of data visualization to verify that weather conditions such as PM2.5, temperature and traffic congestion, and other factors have a certain impact on the demand for taxis in the short term. Then the sample variables are extracted and designed by using the method of constructing variables for short time traffic flow forecasting problem, which lays a foundation for the establishment of the model. Then, the multivariate linear regression model, the stochastic forest regression model and the gradient progressive regression tree model are constructed. Based on these three models, a linear variable weight combination forecasting model is proposed to forecast the taxi demand. The weight of the combined prediction model is determined according to the mean square reciprocal of the historical prediction error of the single model, and the weight of the model is adjusted in real time. Finally, taking the data of the regions with the largest taxi demand in a first-tier city in China as an example, the validity of the combined forecasting model is verified. The experimental results show that the overall prediction accuracy of the combined forecasting model with linear variable weights is higher than that of the single prediction model. In order to ensure the generality and convenience of the model, a taxi demand forecasting system based on Java language and R language is implemented by Rserve. Considering the response time of the system, the system adopts C / S architecture.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：O212.4

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本文編號(hào)：2060850