本文關鍵詞：基于DDDAS的高速公路異常事件影響范圍仿真分析　出處：《重慶大學》2016年碩士論文　論文類型：學位論文

  更多相關文章： 高速公路 DDDAS 交通仿真 數據同化 粒子濾波

【摘要】：高速公路異常事件(如車輛故障、交通事故等)會降低路段通行效率,在車流量較大的情況下,可能會引發(fā)道路交通阻塞和車輛排隊的問題。異常事件的影響范圍和發(fā)展趨勢的可靠估計是制定針對性交通管控策略的前提和基礎,對保障高速公路的暢通運行和提高高速公路的管理服務水平具有重要的現實意義。目前高速公路異常事件的影響范圍主要是通過交通流理論建立預測模型來進行估計,由于現有交通參數檢測精度無法滿足模型的輸入要求尚難以在工程中進行應用。針對此問題論文引入仿真分析技術,對高速公路交通流時間關聯(lián)特性進行分析,并結合歷史車檢器數據特性提出了基于VISSIM仿真系統(tǒng)的交通流參數標定方法和駕駛行為參數校正方法。在此基礎上,結合對粒子濾波算法的深入分析,研究了基于DDDAS的高速公路異常事件影響范圍仿真分析方法。論文主要內容包括:(1)仿真模型交通流參數標定和駕駛行為參數校正。在對高速公路交通流時間關聯(lián)特性分析的基礎上,結合歷史車檢器數據對仿真模型交通流參數進行了標定;針對仿真模型駕駛行為參數默認值標定不準確的情況,結合單因素差方法進行敏感性分析確定用于校正的核心參數,研究了基于遺傳算法的仿真模型駕駛行為參數校正方法;最后利用實際車檢器數據進行了模型驗證。結果表明建立的仿真模型能準確的對道路上的交通流運行趨勢進行仿真。(2)研究基于粒子濾波算法的交通仿真模型數據同化方法。結合交通波理論和閾值理論,建立高速公路車檢器數據預處理方法。在此基礎上結合DDDAS范式和粒子濾波理論,研究了基于DDDAS的高速公路異常事件仿真分析方法,最后對模型的有效性進行了算例驗證。結果表明,基于粒子濾波的交通仿真模型能夠不斷地同化實時數據,實現對道路上堵塞事件位置和實時排隊長度的精確估計。最后介紹了基于粒子濾波算法的交通仿真系統(tǒng)的設計與實現,并結合G75高速北碚隧道段車檢器數據,選取典型真實交通異常事件構建相應的仿真場景,驗證了基于DDDAS的高速公路異常事件影響范圍仿真系統(tǒng)的有效性。結果表明:本文方法可以準確地對異常事件引起的排隊長度進行估計。
[Abstract]:Expressway abnormal events (such as vehicle failures, traffic accidents, etc.) will reduce the efficiency of road sections, in the case of large traffic flow. The problem of road traffic jam and vehicle queuing may be caused. The reliable estimation of the influence range and development trend of abnormal events is the premise and foundation of formulating targeted traffic control strategy. It is of great practical significance to ensure the smooth operation of expressway and to improve the level of management and service of expressway. At present, the influence of abnormal events on expressway is mainly carried out through the establishment of prediction model based on traffic flow theory. Estimate. Because the existing precision of traffic parameter detection can not meet the input requirements of the model, it is difficult to be applied in engineering. In order to solve this problem, the paper introduces simulation analysis technology to analyze the characteristics of time correlation of expressway traffic flow. The calibration method of traffic flow parameters and the method of correcting driving behavior parameters based on VISSIM simulation system are proposed based on the data characteristics of historical vehicle detector. On this basis, the particle filter algorithm is deeply analyzed. This paper studies the simulation and analysis method of the influence range of expressway abnormal events based on DDDAS. The main contents of this paper are as follows: 1). The traffic flow parameters calibration and driving behavior parameters calibration of the simulation model. Based on the analysis of the time correlation characteristics of expressway traffic flow. The traffic flow parameters of the simulation model are calibrated with the historical vehicle detector data. In view of the inaccurate calibration of the default values of driving behavior parameters in the simulation model, the core parameters for correction are determined by sensitivity analysis combined with the single factor difference method. The driving behavior parameters correction method of simulation model based on genetic algorithm is studied. Finally, the model is verified by using the actual vehicle detector data. The results show that the established simulation model can accurately simulate the traffic flow running trend on the road. The data assimilation method of traffic simulation model based on particle filter algorithm is studied. The traffic wave theory and threshold theory are combined. Based on the DDDAS normal form and particle filter theory, the simulation analysis method of highway abnormal events based on DDDAS is studied. Finally, the validity of the model is verified by an example. The results show that the traffic simulation model based on particle filter can assimilate real-time data continuously. Finally, the design and implementation of traffic simulation system based on particle filter algorithm are introduced. And combined with the G75 high-speed Beibei tunnel section vehicle detector data, select typical real traffic anomalies to build the corresponding simulation scene. The effectiveness of the simulation system based on DDDAS is verified. The results show that the proposed method can accurately estimate the queue length caused by abnormal events.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：U491

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相關碩士學位論文 前1條

1 胡滄粟;基于DDDAS的高速公路異常事件影響范圍仿真分析[D];重慶大學;2016年

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本文編號：1408089