本文關(guān)鍵詞：風(fēng)景區(qū)旅游客流量短期預(yù)測方法研究　出處：《合肥工業(yè)大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 灰色關(guān)聯(lián)分析 支持向量回歸（SVR） BP神經(jīng)網(wǎng)絡(luò) 影響因素 旅游客流量預(yù)測

【摘要】：隨著世界經(jīng)濟(jì)的發(fā)展和人民生活水平的提高，旅游業(yè)由此得到了極大的發(fā)展。旅游項(xiàng)目的產(chǎn)品定位和整體規(guī)劃對行業(yè)的發(fā)展有著深遠(yuǎn)的影響，科學(xué)制定旅游業(yè)的持續(xù)發(fā)展戰(zhàn)略規(guī)劃就顯得尤為重要。而旅游客流量短期預(yù)測工作是其中一個(gè)非常重要的環(huán)節(jié)。每日客流量的預(yù)測值一直是景區(qū)管理者在制定政策和日常管理工作中希望了解到的數(shù)據(jù)。在旅游旺季，準(zhǔn)確的短期客流量預(yù)測可以讓旅游管理部門和管理者在物質(zhì)、交通、服務(wù)配備等方面更好的進(jìn)行合理、科學(xué)的調(diào)度和規(guī)劃。 旅游客流量的預(yù)測，尤其是短期客流量的預(yù)測工作是比較繁雜而不確定的。預(yù)測理論和預(yù)測模型的選擇對預(yù)測結(jié)果的準(zhǔn)確性將產(chǎn)生很大的影響,因此本文在預(yù)測理論、預(yù)測的影響因素方面和預(yù)測模型進(jìn)行了相關(guān)的研究。文中對黃山風(fēng)景區(qū)信息化建設(shè)的重點(diǎn)項(xiàng)目——“智慧黃山風(fēng)景區(qū)客流量預(yù)測系統(tǒng)”進(jìn)行了詳細(xì)分析。這為旅游景區(qū)短期客流量預(yù)測工作提供了相關(guān)的方法，各旅游景區(qū)的政策制定和日常管理工作也可對文中一些建議進(jìn)行參考。本文主要的研究內(nèi)容如下： （1）闡述了旅游需求影響因素的國內(nèi)外研究現(xiàn)狀，灰色關(guān)聯(lián)分析理論在旅游行業(yè)中的應(yīng)用現(xiàn)狀，以及旅游需求預(yù)測模型的國內(nèi)外研究現(xiàn)狀，針對短期微觀旅游需求預(yù)測建立了相關(guān)的研究路線。 （2）對旅游需求預(yù)測的影響因素進(jìn)行分析研究，，闡述了旅游需求預(yù)測的相關(guān)概念，分析了長期客流量影響因素和日客流量影響因素兩個(gè)方面，提出篩選影響因素的相關(guān)原則，并在此基礎(chǔ)上給出基于灰色關(guān)聯(lián)分析的影響因素的篩選方法。 （3）對旅游需求的預(yù)測模型進(jìn)行了分析研究，介紹了支持向量回歸（SVR）和BP神經(jīng)網(wǎng)絡(luò)這兩種預(yù)測模型的基本原理，并建立相關(guān)的預(yù)測模型。 （4）以黃山風(fēng)景區(qū)為例進(jìn)行具體的預(yù)測工作，針對風(fēng)景區(qū)獲得的數(shù)據(jù)進(jìn)行收集、整理和分析，從中選取一些對客流量有影響的關(guān)鍵因素，然后采用灰色關(guān)聯(lián)度對結(jié)果排序和進(jìn)行影響因素的篩選，最終選取SVR和BP神經(jīng)網(wǎng)絡(luò)對短期客流量進(jìn)行預(yù)測，進(jìn)而分析了預(yù)測結(jié)果。 本文研究了前人在風(fēng)景區(qū)客流量預(yù)測方面的成果，采用相關(guān)的預(yù)測理論和模型對黃山景區(qū)預(yù)測系統(tǒng)進(jìn)行分析和驗(yàn)證，希望能為今后各大景區(qū)在客流量預(yù)測方面提供借鑒和指導(dǎo)。
[Abstract]:With the development of world economy and the improvement of people's living standard, the tourism industry has got great development. There is a great influence on the development of tourism project product positioning and overall planning of the industry, the scientific development of the tourism industry sustainable development strategic planning is particularly important. While tourism flow forecasting is one very important. To predict the daily traffic is always the value of tourist scenic spot management in the formulation of policies and the daily management work to understand the data. In the tourist season, accurate short-term traffic prediction can make tourism management departments and managers in the material, transportation, services are equipped with better scientific and reasonable the scheduling and planning.
Forecast of tourist flow, especially the prediction of short-term passenger flow is more complicated and uncertain. The prediction theory and model selection on the accuracy of the predicted results will have a huge impact, based on the forecasting theory, model and forecast the influence factors of the related research. The informatization construction in Mount Huangshan scenic areas of key projects -- "the wisdom of Mount Huangshan scenic area traffic prediction system" are analyzed in detail. This work provides a relevant method for prediction of short-term passenger flow of tourist attractions, the tourism policy formulation and daily management work can also be a reference for some suggestions in this paper. The main research contents of this article the following:
(1) elaborated the domestic and foreign research status of tourism demand influencing factors, the application status of grey relational analysis theory in tourism industry, and the domestic and foreign research situation of tourism demand prediction model, and established related research routes for short-term micro tourism demand prediction.
(2) to study the influencing factors on tourism demand forecasting, expounds the related concepts of tourism demand forecasting, analyzes two factors influence factors of long-term traffic and traffic influence, put forward related factors influence selection principle, and on the basis of this screening method is given based on the grey relational analysis influence factors.
(3) the prediction model of tourism demand is analyzed and studied. The basic principles of two prediction models of support vector regression (SVR) and BP neural network are introduced, and relevant prediction models are established.
(4) in the Mount Huangshan scenic area as an example to predict the concrete work, in the scenic area obtained data collection, collation and analysis, select the key factors of passenger flow from it, then using the grey correlation of results ranking and the factors influencing the selection, final selection of SVR and BP neural network prediction for short-term traffic, and then analyzes the prediction results.
This paper studies the predecessors' achievements in the prediction of tourist volume in scenic area, analyzes and verifies the prediction system of Mount Huangshan scenic area by using relevant prediction theories and models, hoping to provide reference and guidance for future scenic spots in the prediction of passenger volume.

【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F592.7;F224

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