本文選題：公共自行車系統(tǒng) + 自行車數(shù)目預(yù)測��； 參考：《南京師范大學(xué)》2015年碩士論文

【摘要】：公共自行車系統(tǒng)能有效的解決人們出行中“最后一公里”的問題,能更好地提升城市公共交通的整體服務(wù)水平。然而,“借車難,還車難”是公共自行車系統(tǒng)的主要問題之一,直接影響著用戶的滿意度。自行車調(diào)度是解決這些的有效方法之一,而租賃點(diǎn)的自行車數(shù)目預(yù)測是自行車調(diào)度的核心問題之一。因此,本文針對公共自行車系統(tǒng)特點(diǎn),進(jìn)行租賃點(diǎn)自行車數(shù)目預(yù)測研究,具有重要的理論意義與應(yīng)用價(jià)值。(1)基于租賃點(diǎn)的自行車數(shù)目的變化規(guī)律分析,本文提出了一種新的租賃點(diǎn)自行車數(shù)目的預(yù)測框架。該框架包括結(jié)合數(shù)據(jù)選擇,預(yù)測模型和誤差補(bǔ)償三部分,以預(yù)測模型為基礎(chǔ),結(jié)合誤差補(bǔ)償機(jī)制,能大大的提高預(yù)測的精度。(2)為了挖掘公共自行車各租賃點(diǎn)的變化規(guī)律,給預(yù)測模型提供良好的數(shù)據(jù)支持,本文提出了基于自行車使用規(guī)模的公共自行車租賃點(diǎn)的聚類方法。通過對不同天氣(晴天、陰天、大雨、大雪、大霧、大風(fēng)等)、季節(jié)、日期類型以及不同租賃點(diǎn)的分析,結(jié)合溫州市鹿城區(qū)公共自行車系統(tǒng)的實(shí)際運(yùn)行數(shù)據(jù),對各種類型租賃點(diǎn)在不同外部條件下的變化曲線進(jìn)行描述和分析,并將變化規(guī)律和幅度表示為特征串,對租賃點(diǎn)進(jìn)行聚類。(3)本文提出了一個(gè)基于時(shí)間序列的公共自行車租賃點(diǎn)自行車數(shù)目預(yù)測模型。在現(xiàn)有公共自行車系統(tǒng)租賃點(diǎn)自行車預(yù)測模型的基礎(chǔ)上,本文采用基于租賃點(diǎn)聚類方法的數(shù)據(jù)選擇方式,并結(jié)合租賃點(diǎn)歷史趨勢,對公共自行車系統(tǒng)租賃點(diǎn)中的自行車數(shù)目進(jìn)行預(yù)測。通過實(shí)際數(shù)據(jù)的實(shí)驗(yàn)結(jié)果與現(xiàn)有模型的預(yù)測結(jié)果進(jìn)行對比,本文的預(yù)測模型具有較高的準(zhǔn)確性。(4)針對預(yù)測模型在數(shù)據(jù)選擇過程中,所選擇相似的歷史數(shù)據(jù)因影響因素存在差異而產(chǎn)生的誤差,本文提出了公共自行車預(yù)測結(jié)果的誤差補(bǔ)償方法。通過對可能產(chǎn)生誤差的因素進(jìn)行分析,并對這些因素的具體影響大小進(jìn)行量化,結(jié)合在預(yù)測模型中使用到的歷史數(shù)據(jù),運(yùn)用誤差補(bǔ)償方法計(jì)算出存在的誤差值,并補(bǔ)償?shù)筋A(yù)測模型的結(jié)果中。實(shí)驗(yàn)表明,經(jīng)過誤差補(bǔ)償以后的預(yù)測結(jié)果精確度更高。
[Abstract]:The public bicycle system can effectively solve the "last kilometer" problem in people's travel, and improve the overall service level of urban public transport.However, it is one of the main problems of public bicycle system that it is difficult to borrow or return a car, which directly affects the satisfaction of users.Bicycle scheduling is one of the effective methods to solve these problems, and the prediction of bicycle number at rental point is one of the core problems of bicycle scheduling.Therefore, according to the characteristics of public bicycle system, this paper studies the prediction of bicycle number at rental point, which has important theoretical significance and application value.This paper presents a new prediction framework for the number of bicycles at rental points.The framework includes three parts: data selection, prediction model and error compensation. Based on the prediction model and error compensation mechanism, it can greatly improve the accuracy of prediction.In order to provide good data support for the prediction model, this paper proposes a clustering method based on the scale of bicycle use for public bicycle rental points.Through the analysis of different weather (sunny, cloudy, heavy rain, Greater Snow, fog, strong wind, etc.), seasons, date types and different rental points, combined with the actual operation data of public bicycle system in Lucheng District of Wenzhou City,The variation curves of various types of lease points under different external conditions are described and analyzed, and the law and amplitude of change are expressed as characteristic strings.This paper presents a time series based prediction model for the number of bicycles in public bicycle rental points.On the basis of the existing prediction model of bicycle rental point in public bicycle system, this paper adopts the data selection method based on rent-point clustering method, and combines the historical trend of rental point.The number of bicycles in the rental point of the public bicycle system is predicted.By comparing the experimental results of the actual data with the prediction results of the existing models, the prediction model in this paper has a high accuracy.This paper presents an error compensation method for the prediction result of public bicycle, which is caused by the difference of influencing factors in the similar historical data.By analyzing the factors that may produce errors and quantifying the specific influence of these factors, combining with the historical data used in the prediction model, the error compensation method is used to calculate the error.And compensation to the results of the prediction model.The experimental results show that the accuracy of the prediction results after error compensation is higher.
【學(xué)位授予單位】：南京師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.225

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本文編號：1755585