本文選題：快速公交 + 數(shù)據(jù)分析��； 參考：《西南交通大學(xué)》2017年碩士論文

【摘要】：快速公交系統(tǒng)大多采用閘機(jī)刷卡,進(jìn)站乘車的乘車方式,而且車輛靈活運(yùn)營,較之普通公交,車輛的運(yùn)營信息通常是不易獲取的信息。本文在僅依靠快速公交乘客刷卡數(shù)據(jù)的情況下,對車輛運(yùn)營信息進(jìn)行推算,并根據(jù)推算所得數(shù)據(jù)進(jìn)行統(tǒng)計(jì)分析,對公交服務(wù)效果作出評價(jià),并對公交運(yùn)營管理提供相關(guān)建議。本文使用Matlab 2012b進(jìn)行數(shù)據(jù)處理和算法構(gòu)建和執(zhí)行,使用SPSS Statistics 22進(jìn)行站點(diǎn)客流統(tǒng)計(jì)分析。本文創(chuàng)新性研究工作可概括為以下三個(gè)方面:第一,在缺乏GPS等其他類型數(shù)據(jù)的情況下,僅基于成都市快速公交一周內(nèi)的乘客刷卡數(shù)據(jù),在進(jìn)行基本數(shù)據(jù)整理和統(tǒng)計(jì)分析過后,通過聚類算法推算出車輛運(yùn)營信息,包括各個(gè)時(shí)間段運(yùn)營的車次數(shù)量、車輛運(yùn)營時(shí)刻表以及各個(gè)車次在沿線站點(diǎn)的上下車人數(shù)等難以獲取的數(shù)據(jù)信息。第二,根據(jù)車輛運(yùn)營信息計(jì)算并統(tǒng)計(jì)各項(xiàng)滿載率指標(biāo),然后基于車輛滿載情況進(jìn)行客流流向和客流量統(tǒng)計(jì)。并且通過集散量的統(tǒng)計(jì)分析和系統(tǒng)聚類方法選出各區(qū)域代表站點(diǎn),提出以代表站點(diǎn)預(yù)測其他站點(diǎn)集散量的客流預(yù)測方法。并且選用其他日期的數(shù)據(jù)與預(yù)測數(shù)據(jù)進(jìn)行了對比,證明了結(jié)果的可行性與準(zhǔn)確性。第三,根據(jù)車輛運(yùn)營信息計(jì)算乘客等車時(shí)間、乘客乘車過程中經(jīng)歷的平均滿載率和乘客平均每公里通勤時(shí)間等對公交乘客滿意度有重要的幾個(gè)指標(biāo)。對各個(gè)指標(biāo)進(jìn)行統(tǒng)計(jì)分析過后,用K均值的方法將乘車感受不同的乘客分為若干類別,識(shí)別出乘客滿意度較差的乘客。與傳統(tǒng)調(diào)查問卷的方式不同,直接通過數(shù)據(jù)量化指標(biāo)可更利于公交優(yōu)化研究,對于公交系統(tǒng)運(yùn)營更具管理意義。
[Abstract]:The bus rapid transit system mostly uses the gate machine to swipe the card, enters the station to take the bus the way, moreover the vehicle is nimble operation, compared with the ordinary public transport, the vehicle operation information is usually difficult to obtain the information. Based on the data of bus Rapid Transit (BRT) passengers swiping cards, this paper calculates the operation information of the vehicles, and makes statistical analysis according to the calculated data to evaluate the effect of bus service, and provides relevant suggestions for bus operation management. In this paper, Matlab 2012b is used for data processing and algorithm construction and execution, and SPSS Statistics 22 is used for statistical analysis of station passenger flow. The innovative research work in this paper can be summarized as follows: first, in the absence of GPS and other types of data, only based on the data of passengers swiping cards within one week of Chengdu bus Rapid Transit, after the basic data collation and statistical analysis, The operation information of vehicles is calculated by clustering algorithm, including the number of trains in operation in each time period, the running schedule of vehicles and the number of people on and off each train at the station along the route, and so on, which are difficult to obtain. Secondly, according to the vehicle operation information, the full load rate index is calculated and counted, and then the passenger flow direction and passenger flow statistics are carried out based on the vehicle full load situation. Through the statistical analysis of the distribution amount and the systematic clustering method, the representative stations of each region are selected, and the passenger flow forecasting method is put forward to predict the distribution of other stations on behalf of the stations. The feasibility and accuracy of the results are proved by comparing the data of other dates with the predicted data. Thirdly, according to the vehicle operation information, the passenger waiting time, the average full load rate and the average commuting time per kilometer have several important indicators for bus passenger satisfaction. After the statistical analysis of each index, the passengers with different sense of travel are divided into several categories by means of K-means method, and the passengers with poor passenger satisfaction are identified. Different from the traditional questionnaire, it can be more beneficial to the study of bus optimization by directly quantifying the data, and it has more management significance for the operation of the public transport system.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F572.88

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本文編號(hào)：1875549