【摘要】：提升公交的服務(wù)水平是實(shí)施公交優(yōu)先的有效方式。隨著智能公交的發(fā)展,在運(yùn)營過程中,公交系統(tǒng)產(chǎn)生大量的數(shù)據(jù),為公交規(guī)劃和管理部門制定決策提供依據(jù)。基于公交IC卡數(shù)據(jù)和公交GPS數(shù)據(jù),通過對數(shù)據(jù)的處理,及數(shù)據(jù)的分析和挖掘,可以獲得有效的客流信息和車輛運(yùn)行中的信息,并做出相應(yīng)的預(yù)測,為公交調(diào)度提供決策支持。 本文首先介紹了對公交數(shù)據(jù)源的分析,介紹智能交通背景下的公交智能系統(tǒng)產(chǎn)生數(shù)據(jù)的模式；公交數(shù)據(jù)的預(yù)處理流程,以IC卡數(shù)據(jù)和GPS數(shù)據(jù)為主,介紹了兩種數(shù)據(jù)的不同的數(shù)據(jù)預(yù)處理步驟。本文研究了對公交數(shù)據(jù)的數(shù)據(jù)挖掘及分析。首先介紹了數(shù)據(jù)挖掘的基本概念和常用算法；對于IC卡的數(shù)據(jù)挖掘,本文分別研究了基于IC卡的客流時(shí)段劃分,一些客流指標(biāo)的統(tǒng)計(jì)分析,以及基于BP神經(jīng)網(wǎng)絡(luò)的客流預(yù)測；對于公交GPS的數(shù)據(jù)挖掘,本文研究了公交GPS的路段匹配,公交在運(yùn)行過程中的運(yùn)行特性的分析,以及基于BP神經(jīng)網(wǎng)絡(luò)的公交運(yùn)行時(shí)間的預(yù)測。 基于對公交數(shù)據(jù)的挖掘,本文研究了公交調(diào)度,包括靜態(tài)調(diào)度及調(diào)度問題。對于靜態(tài)調(diào)度,本文主要研究了時(shí)刻表的編制部分,即首先根據(jù)IC客流數(shù)據(jù),劃分客流時(shí)段,基于乘客等待成本最小、擁擠度最小、公交公司運(yùn)營成本最小,建立公交時(shí)刻表的編制模型,并根據(jù)遺傳算法進(jìn)行求解；對于動(dòng)態(tài)調(diào)度,本文分別研究了異常事件下的車輛調(diào)度形式,以及實(shí)時(shí)調(diào)度中的站點(diǎn)調(diào)度和站點(diǎn)間調(diào)度,即動(dòng)態(tài)滯站調(diào)度和公交信號優(yōu)先的動(dòng)態(tài)調(diào)度,減少串車和大間隔的發(fā)生,提高公交的服務(wù)水平。 根據(jù)以上研究內(nèi)容,本文給出實(shí)例分析,分別以實(shí)例驗(yàn)證了以上的數(shù)據(jù)處理、數(shù)據(jù)挖掘、分析,以及公交調(diào)度的研究內(nèi)容。
[Abstract]:Improving the service level of public transport is an effective way to implement bus priority. With the development of intelligent public transport, the public transport system produces a lot of data in the operation process, which provides the basis for the public transport planning and management department to make decisions. Based on bus IC card data and bus GPS data, through the data processing, data analysis and mining, we can obtain effective passenger flow information and vehicle operation information, and make the corresponding prediction, and provide decision support for bus scheduling. This paper first introduces the analysis of bus data sources, and introduces the data generation model of intelligent bus system in the context of intelligent transportation. Based on IC card data and GPS data, the different data preprocessing steps of two kinds of data are introduced. This paper studies the data mining and analysis of bus data. Firstly, the basic concepts and common algorithms of data mining are introduced. For IC card data mining, this paper studies the division of passenger flow time based on IC card, the statistical analysis of some passenger flow indexes, and the prediction of passenger flow based on BP neural network. For the data mining of bus GPS, this paper studies the section matching of bus GPS, the analysis of the running characteristics of public transport during operation, and the prediction of bus running time based on BP neural network. Based on the mining of bus data, this paper studies bus scheduling, including static scheduling and scheduling problems. For static scheduling, this paper mainly studies the compiling part of the timetable, that is, according to the IC passenger flow data, the passenger flow period is divided, based on the minimum passenger waiting cost, the minimum congestion, the minimum operating cost of the bus company. The model of bus timetable is established and solved by genetic algorithm. For dynamic scheduling, this paper studies the vehicle scheduling form under abnormal events, and the station scheduling and inter-site scheduling in real-time scheduling, that is, dynamic stop scheduling and bus signal priority dynamic scheduling. Reduce the occurrence of train strings and large intervals, improve the level of public transport services. According to the above research content, this paper gives the case analysis, and verifies the above data processing, data mining, analysis, and bus dispatch research content.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U491.17

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