本文選題：公交車 + GPS�。� 參考：《太原理工大學(xué)》2017年碩士論文

【摘要】：“智慧公交”是“智慧城市”的重要組成部分,是解決城市交通問(wèn)題和方便居民出行的有效途徑。智慧交通不僅可以誘導(dǎo)出行,還可以通過(guò)歷史大數(shù)據(jù)的分析決策出行。公交客流量是深度挖掘交通出行大數(shù)據(jù)、研究乘客出行模式的基礎(chǔ)。公交車到站時(shí)間更是出行者最為關(guān)心的交通信息之一。因此,以地理信息系統(tǒng)和數(shù)據(jù)分析為手段,展開(kāi)對(duì)公交出行分析及挖掘工作,結(jié)合公交車數(shù)據(jù)結(jié)構(gòu),探討乘客上下車站點(diǎn)推斷和公交車到站時(shí)間預(yù)測(cè)方法,對(duì)城市交通問(wèn)題的解決具有積極意義。本文在綜合分析國(guó)內(nèi)外對(duì)客流量和出行鏈研究方法的適用性、公交到站時(shí)刻模擬預(yù)測(cè)速度優(yōu)缺點(diǎn)的基礎(chǔ)上,結(jié)合數(shù)據(jù)源特點(diǎn)和人力財(cái)力,提出以單條出行鏈為研究對(duì)象,研究確定各站點(diǎn)吸引權(quán),計(jì)算站點(diǎn)客流量;建立多元線性回歸模型計(jì)算公交車歷史平均車速,綜合瞬時(shí)速度和到站距離,計(jì)算修正平均速度,預(yù)測(cè)公交車到站時(shí)間�；谏钲谑蠥FC和GPS數(shù)據(jù),利用時(shí)間匹配和密度聚類方法確定乘客上車站點(diǎn);分析乘客出行行為以及規(guī)律,引入出行鏈單元公交節(jié)的概念。公交出行節(jié)連續(xù)時(shí),依據(jù)乘坐人下次乘車的上車位置判斷乘客下車站點(diǎn);公交出行節(jié)斷裂的乘客,結(jié)合乘客刷卡高頻站點(diǎn)的頻次和公交路線下游各站點(diǎn)吸引權(quán)重,判別出行節(jié)斷裂時(shí)乘客下車位置坐標(biāo)的可能性,并設(shè)計(jì)推斷乘客下車站點(diǎn)算法。根據(jù)預(yù)測(cè)得到的乘客上下車站點(diǎn)信息,統(tǒng)計(jì)估算車內(nèi)人數(shù)。利用K最鄰近結(jié)點(diǎn)的方法對(duì)道路進(jìn)行分段,建立多元回歸速度模型估計(jì)各路段平均速度,以計(jì)算結(jié)果為歷史數(shù)據(jù)依據(jù),結(jié)合公交實(shí)時(shí)瞬時(shí)速度和距離到達(dá)站點(diǎn)的距離長(zhǎng)度,預(yù)測(cè)公交的到站時(shí)刻。根據(jù)公交乘客下車站點(diǎn)推斷算法,實(shí)例分析并預(yù)測(cè)結(jié)果,計(jì)算下游各站點(diǎn)的乘客可能的下車頻次和分析乘客高頻下車站點(diǎn)集,分析算法可行性,根據(jù)乘客下車預(yù)測(cè)點(diǎn)與真實(shí)下車站點(diǎn)之間的距離和各個(gè)預(yù)測(cè)點(diǎn)的權(quán)重判別評(píng)估預(yù)測(cè)的準(zhǔn)確性,經(jīng)過(guò)驗(yàn)證,表明方法是有效的。依據(jù)到站時(shí)間預(yù)測(cè)模型計(jì)算實(shí)際公交到站時(shí)間,通過(guò)與真實(shí)值對(duì)比評(píng)估,表明誤差在合理范圍內(nèi)。利用路段平均速度的計(jì)算結(jié)果建立數(shù)據(jù)庫(kù),并對(duì)道路通暢性進(jìn)行級(jí)別劃分和實(shí)時(shí)可視化表達(dá),其結(jié)論符合實(shí)際狀態(tài)。
[Abstract]:"Smart bus" is an important part of "Smart City", which is an effective way to solve urban traffic problems and facilitate residents to travel. Intelligent transportation can not only induce travel, but also travel through historical big data's analysis and decision. Public transport passenger flow is the basis of deeply excavating traffic travel big data and studying passenger travel mode. Bus arrival time is one of the most concerned traffic information for passengers. Therefore, by means of GIS and data analysis, the analysis and mining of bus trip are carried out, and combined with the bus data structure, the methods of estimating the stop and the arrival time of the bus are discussed. It is of positive significance to solve the urban traffic problems. On the basis of synthetically analyzing the applicability of domestic and foreign research methods of passenger flow and trip chain, and the advantages and disadvantages of simulating and predicting the speed of bus arrival time, combined with the characteristics of data sources and human and financial resources, this paper puts forward a single trip chain as the research object. The research determines the attraction right of each station, calculates the passenger flow of the station, establishes the multivariate linear regression model to calculate the bus historical average speed, synthesizes the instantaneous speed and the distance to the station, calculates the revised average speed, and predicts the bus arrival time. Based on the data of AFC and GPS in Shenzhen City, the method of time matching and density clustering is used to determine the passenger boarding station, the travel behavior and regularity of passengers are analyzed, and the concept of public transport section of trip chain unit is introduced. When the bus travel section is continuous, the passenger gets off the bus station according to the passenger's next boarding position; the passengers whose bus trip node is broken combine the frequency of the high-frequency station and the attraction weight of the lower reaches of the bus route. The possibility of determining the coordinates of the passenger's alighting position when the trip node is broken and the algorithm of inferring the passenger's stopping station are designed. According to the forecast of the passenger station information, statistics estimate the number of people in the car. Using the method of nearest node of K to segment the road, a multivariate regression speed model is established to estimate the average speed of each section. Based on the historical data, the real-time instantaneous speed of public transportation and the distance length from the arrival station are combined. Predict the arrival time of the bus. According to the algorithm of bus passenger station inference, the example analysis and prediction result, the calculation of the possible frequency of passengers getting off the lower reaches and the analysis of passenger high frequency station set, the feasibility of the algorithm is analyzed. According to the distance between the prediction point of passenger and the real station and the weight of each prediction point, the accuracy of evaluation and prediction is evaluated and verified, which shows that the method is effective. According to the prediction model of arrival time, the actual bus arrival time is calculated. By comparing with the real value, the error is within a reasonable range. The database is established by using the calculation results of the average speed of the road section, and the road flow is classified and visualized in real time. The conclusion is in line with the actual state.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;U491.17

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