【摘要】：隨著智慧城市的發(fā)展,對城市公交技術(shù)提出了更高的要求,城市公交系統(tǒng)的相關(guān)信息處理問題是如今研究的熱點(diǎn)問題。如今研究者無論是在硬件還是軟件上都做了許多相關(guān)技術(shù)的研究,也取得了一定的成果,其主要體現(xiàn)于GPS北斗導(dǎo)航定位,線路調(diào)度等,以方便了乘客。但就智能城市而言,卻無法準(zhǔn)確獲得車上具體的信息,智能性不強(qiáng)。城市公交系統(tǒng)與調(diào)度中心不能做出實(shí)時的調(diào)整,調(diào)度的決策僅限于對線路的調(diào)度,對客流量的預(yù)測即是通過以往數(shù)據(jù)的猜測和估算,并沒有很好的將智能性和實(shí)時性體現(xiàn)出來。本文將行為模式的智能性引入城市公交系統(tǒng)中,以行為模式分析理論為基礎(chǔ),通過分析車輛在運(yùn)行過程中的行為,建立了車輛的行為模型,通過傳感器模塊提取車輛當(dāng)前行為特征數(shù)據(jù),如車輛速度、車門的開和關(guān)、車輛當(dāng)前GPS位置數(shù)據(jù)、實(shí)時乘客人數(shù)等。利用這些行為特征數(shù)據(jù)確定車輛的行為模式,根據(jù)行為模式來制定相應(yīng)的控制和管理方案,提高了城市公交的智能性和靈活性,更加有效和方便的管理車輛。行為模式包括乘客行為模式和車輛行為模式等。乘客行為模式識別是通過實(shí)時乘客行為模式來確定車的載荷即人數(shù)計量。然而現(xiàn)有的車的載荷計量方法不準(zhǔn)確,并沒有很好的實(shí)時性,不能很好的滿足行為模式識別的需求。本文通過行為模式分析乘客在上下車時的行為,建立乘客的行為模型,通過多種傳感器提取了乘客上下車時的行為特征,如壓力變化、乘客與車輛之間距離、人臉朝向等行為特征數(shù)據(jù)。并對特征數(shù)據(jù)進(jìn)行分類,定義了乘客的行為,進(jìn)而完成人數(shù)的實(shí)時計量。為了提高了城市公交的智能性和實(shí)時性,以及準(zhǔn)確性,本文基于IPv6組播的管理技術(shù),設(shè)計了城市公交的線路和車輛的分組管理方案,使城市公交系統(tǒng)的管理更方便。同時本文針對車輛數(shù)據(jù)的傳輸給出了基于車輛行為的傳輸控制策略和機(jī)制。該機(jī)制通過判定車輛行為模式,動態(tài)控制車輛數(shù)據(jù)傳輸。實(shí)驗(yàn)證明,本文所設(shè)計的乘客人數(shù)計量準(zhǔn)確度達(dá)到90%以上,能很好的向城市公交的智能調(diào)度提供準(zhǔn)確而且實(shí)時的數(shù)據(jù)支持,方便了公交系統(tǒng)的管理,為城市公交向智能化、科學(xué)化發(fā)展奠定了堅實(shí)的基礎(chǔ)。
[Abstract]:With the development of intelligent city, higher demands have been put forward on the technology of urban public transport, and the information processing of urban public transport system is a hot issue. Nowadays, researchers have done a lot of research on hardware and software, and have made some achievements, which are mainly reflected in GPS Beidou navigation, line scheduling and so on, in order to facilitate passengers. But in the case of intelligent city, it can not accurately obtain the specific information on the vehicle, so the intelligence is not strong. Urban public transport system and dispatching center can not make real-time adjustment, the scheduling decision is limited to the dispatch of the line, and the prediction of passenger flow is based on the guesses and estimates of the previous data, and it does not reflect the intelligence and real-time performance very well. In this paper, the intelligent behavior pattern is introduced into the urban public transport system. Based on the theory of behavior pattern analysis, the behavior model of the vehicle is established by analyzing the behavior of the vehicle in the course of operation. The sensor module is used to extract the characteristic data of the vehicle's current behavior, such as the vehicle speed, the opening and closing of the door, the current GPS position data of the vehicle, the real-time number of passengers and so on. Using these behavioral characteristics data to determine the behavior pattern of the vehicle and to formulate the corresponding control and management scheme according to the behavior model, the intelligence and flexibility of the urban public transport are improved, and the vehicle management is more effective and convenient. The behavior pattern includes passenger behavior pattern and vehicle behavior pattern. Passenger behavior pattern recognition is based on real-time passenger behavior pattern to determine the load of the vehicle, that is, the number of measurement. However, the existing load measurement methods are not accurate, and can not meet the requirements of behavior pattern recognition. In this paper, the behavior of passengers during boarding and disembarking is analyzed, and the behavior model of passengers is established. The behavior characteristics of passengers are extracted by a variety of sensors, such as pressure changes, distance between passengers and vehicles. Face orientation isobaric feature data. The characteristic data are classified, and the behavior of passengers is defined, and the real-time measurement of the number of passengers is accomplished. In order to improve the intelligence, real-time and accuracy of urban public transport, this paper designs a group management scheme of urban public transport lines and vehicles based on IPv6 multicast management technology, which makes the management of urban public transport system more convenient. At the same time, this paper gives the transmission control strategy and mechanism based on vehicle behavior for vehicle data transmission. The mechanism dynamically controls vehicle data transmission by determining vehicle behavior patterns. The experiment proves that the measurement accuracy of the passenger number designed in this paper is more than 90%, which can provide accurate and real-time data support to the intelligent dispatch of urban public transport, facilitate the management of the public transport system, and provide intelligence for the urban public transport. Scientific development laid a solid foundation.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U495;U491.17

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本文編號：2122951