本文關(guān)鍵詞：基于一卡通數(shù)據(jù)分析的通勤性定制公交線(xiàn)路規(guī)劃　出處：《吉林大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 通勤 定制公交 一卡通 大數(shù)據(jù) 方式轉(zhuǎn)移 線(xiàn)路規(guī)劃

【摘要】：近年來(lái),大數(shù)據(jù)處理技術(shù)漸趨成熟,給交通行業(yè)的發(fā)展帶來(lái)了變革,它多角度多維度的數(shù)據(jù)展現(xiàn)能力,使我們能夠更加直觀(guān)、清晰地認(rèn)識(shí)居民出行特點(diǎn)、交通需求的時(shí)空分布特征等。如何利用大數(shù)據(jù)呈現(xiàn)的宏觀(guān)數(shù)據(jù)特征,指導(dǎo)城市交通規(guī)劃已然成為交通領(lǐng)域當(dāng)前研究的熱點(diǎn)。論文以一卡通數(shù)據(jù)為基礎(chǔ),運(yùn)用大數(shù)據(jù)處理技術(shù)挖掘通勤性定制公交目標(biāo)服務(wù)群體客流的時(shí)空分布特征,并以此為基礎(chǔ),運(yùn)用單目標(biāo)規(guī)劃模型進(jìn)行通勤性定制公交線(xiàn)路規(guī)劃,旨在在新的數(shù)據(jù)環(huán)境下,更加經(jīng)濟(jì)、直觀(guān)、準(zhǔn)確地把握客流分布特征,精細(xì)化通勤性定制公交線(xiàn)路規(guī)劃,從而為定制公交的推廣使用提供理論基礎(chǔ)。在一卡通數(shù)據(jù)的通勤出行信息識(shí)別方法的研究方面,以上海市一卡通數(shù)據(jù)以及公交車(chē)實(shí)時(shí)運(yùn)行數(shù)據(jù)等數(shù)據(jù)為例,以O(shè)RACLE數(shù)據(jù)庫(kù)為平臺(tái),設(shè)計(jì)了識(shí)別通勤群體、通勤出行起終點(diǎn)以及判斷中途停駐點(diǎn)性質(zhì)的方法,該方法適用于具有相同數(shù)據(jù)結(jié)構(gòu)的其他一卡通數(shù)據(jù)。為了更好的把握公共交通通勤客流向定制公交轉(zhuǎn)移的特點(diǎn),分別建立了公交、地鐵、公交地鐵換乘向通勤性定制公交轉(zhuǎn)移的Logit模型,并運(yùn)用上海市P區(qū)的通勤性出行方式轉(zhuǎn)移意向調(diào)查對(duì)模型進(jìn)行參數(shù)標(biāo)定,標(biāo)定模型能夠在一定程度上反映城市公共交通通勤群體向定制公交轉(zhuǎn)移的客流關(guān)系。在通勤性定制公交線(xiàn)路規(guī)劃方面,以一站直達(dá)優(yōu)先、快速性、準(zhǔn)時(shí)性、舒適性以及經(jīng)濟(jì)性為原則,建立了以經(jīng)濟(jì)性和快速性權(quán)衡最優(yōu)為目標(biāo),以滿(mǎn)載率、站點(diǎn)數(shù)目、到達(dá)時(shí)間等指標(biāo)為約束的優(yōu)化模型,并以標(biāo)準(zhǔn)遺傳算法為基礎(chǔ),設(shè)計(jì)了適用于該問(wèn)題求解的改進(jìn)遺傳算法,模型求解結(jié)果能夠給出較為合理的通勤性定制公交規(guī)劃線(xiàn)路。最后,以上海開(kāi)放數(shù)據(jù)為基礎(chǔ),運(yùn)用上述方法分析了上海市P區(qū)居民的通勤出行特點(diǎn),并規(guī)劃了通勤性定制公交線(xiàn)路,較為良好的實(shí)現(xiàn)了運(yùn)用大數(shù)據(jù)支撐交通規(guī)劃這個(gè)初衷。
[Abstract]:In recent years, big data processing technology gradually matured, to the development of the transportation industry has brought changes, its multi-angle and multi-dimensional data display ability, so that we can more intuitive, clear understanding of the characteristics of residents travel. How to use the macro-data features presented by big data to guide urban traffic planning has become the current research hotspot in the field of transportation. The paper is based on one-card data. Using big data processing technology to excavate the spatial and temporal distribution characteristics of commuting customized public transport target service group passenger flow, and on the basis of this, the single-objective programming model is used to carry out the commuting customized bus route planning. In the new data environment, it is more economical, intuitive and accurate to grasp the characteristics of passenger flow distribution, and to refine the commuting customized bus line planning. In order to provide a theoretical basis for the promotion and use of customized public transport. In the study of commute information identification method of one-card data, take Shanghai one-card data and bus real-time operation data as an example. Based on ORACLE database, this paper designs a method to identify commuting groups, start and finish commuting and judge the nature of stop-point. This method is suitable for other one-card data with the same data structure. In order to better grasp the characteristics of public transport commuter to customized public transport, respectively, the establishment of public transport, subway. The Logit model of transit transfer from transit to commuting customization is proposed and the parameters of the model are calibrated by the investigation of commuting mode transfer intention in P District of Shanghai. The calibration model can reflect to a certain extent the passenger flow relationship between the urban public transport commuter group and the customized public transport. In the aspect of commuting custom bus route planning, one station direct first, fast and punctual. Based on the principle of comfort and economy, the optimization model is established, which is based on standard genetic algorithm (GA). The optimization model is based on the criterion of full load rate, number of stations, arrival time and so on. An improved genetic algorithm is designed for solving the problem. The result of the model can give a reasonable transit planning route. Finally, based on Shanghai open data. This paper analyzes the commuting characteristics of residents in P District of Shanghai by using the above method, and plans the commuting customized bus lines, which realizes the original intention of using big data to support traffic planning.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：U491.17

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