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,展開對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推斷和公交車到站時間預(yù)測方法,對城市交通問題的解決具有積極意義。本文在綜合分析國內(nèi)外對客流量和出行鏈研究方法的適用性、公交到站時刻模擬預(yù)測速度優(yōu)缺點的基礎(chǔ)上,結(jié)合數(shù)據(jù)源特點和人力財力,提出以單條出行鏈為研究對象,研究確定各站點吸引權(quán),計算站點客流量;建立多元線性回歸模型計算公交車歷史平均車速,綜合瞬時速度和到站距離,計算修正平均速度,預(yù)測公交車到站時間�；谏钲谑蠥FC和GPS數(shù)據(jù),利用時間匹配和密度聚類方法確定乘客上車站點;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,依據(jù)乘坐人下次乘車的上車位置判斷乘客下車站點;公交出行節(jié)斷裂的乘客,結(jié)合乘客刷卡高頻站點的頻次和公交路線下游各站點吸引權(quán)重,判別出行節(jié)斷裂時乘客下車位置坐標(biāo)的可能性,并設(shè)計推斷乘客下車站點算法。根據(jù)預(yù)測得到的乘客上下車站點信息,統(tǒng)計估算車內(nèi)人數(shù)。利用K最鄰近結(jié)點的方法對道路進行分段,建立多元回歸速度模型估計各路段平均速度,以計算結(jié)果為歷史數(shù)據(jù)依據(jù),結(jié)合公交實時瞬時速度和距離到達站點的距離長度,預(yù)測公交的到站時刻。根據(jù)公交乘客下車站點推斷算法,實例分析并預(yù)測結(jié)果,計算下游各站點的乘客可能的下車頻次和分析乘客高頻下車站點集,分析算法可行性,根據(jù)乘客下車預(yù)測點與真實下車站點之間的距離和各個預(yù)測點的權(quán)重判別評估預(yù)測的準(zhǔn)確性,經(jīng)過驗證,表明方法是有效的。依據(jù)到站時間預(yù)測模型計算實際公交到站時間,通過與真實值對比評估,表明誤差在合理范圍內(nèi)。利用路段平均速度的計算結(jié)果建立數(shù)據(jù)庫,并對道路通暢性進行級別劃分和實時可視化表達,其結(jié)論符合實際狀態(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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U495;U491.17

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