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  本文選題：公共自行車系統(tǒng) + 自行車數(shù)目預測��； 參考：《南京師范大學》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,而租賃點的自行車數(shù)目預測是自行車調(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,進行租賃點自行車數(shù)目預測研究,具有重要的理論意義與應用價值。(1)基于租賃點的自行車數(shù)目的變化規(guī)律分析,本文提出了一種新的租賃點自行車數(shù)目的預測框架。該框架包括結(jié)合數(shù)據(jù)選擇,預測模型和誤差補償三部分,以預測模型為基礎(chǔ),結(jié)合誤差補償機制,能大大的提高預測的精度。(2)為了挖掘公共自行車各租賃點的變化規(guī)律,給預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的聚類方法。通過對不同天氣(晴天、陰天、大雨、大雪、大霧、大風等)、季節(jié)、日期類型以及不同租賃點的分析,結(jié)合溫州市鹿城區(qū)公共自行車系統(tǒng)的實際運行數(shù)據(jù),對各種類型租賃點在不同外部條件下的變化曲線進行描述和分析,并將變化規(guī)律和幅度表示為特征串,對租賃點進行聚類。(3)本文提出了一個基于時間序列的公共自行車租賃點自行車數(shù)目預測模型。在現(xiàn)有公共自行車系統(tǒng)租賃點自行車預測模型的基礎(chǔ)上,本文采用基于租賃點聚類方法的數(shù)據(jù)選擇方式,并結(jié)合租賃點歷史趨勢,對公共自行車系統(tǒng)租賃點中的自行車數(shù)目進行預測。通過實際數(shù)據(jù)的實驗結(jié)果與現(xiàn)有模型的預測結(jié)果進行對比,本文的預測模型具有較高的準確性。(4)針對預測模型在數(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測結(jié)果的誤差補償方法。通過對可能產(chǎn)生誤差的因素進行分析,并對這些因素的具體影響大小進行量化,結(jié)合在預測模型中使用到的歷史數(shù)據(jù),運用誤差補償方法計算出存在的誤差值,并補償?shù)筋A測模型的結(jié)果中。實驗表明,經(jīng)過誤差補償以后的預測結(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.
【學位授予單位】：南京師范大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491.225

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本文編號：1755585