本文選題：微觀交通仿真 + 參數(shù)校正　； 參考：《吉林大學》2015年碩士論文

【摘要】：隨著城市的快速發(fā)展，居民私家車保有量逐年遞增，交通擁堵問題已經(jīng)在很多城市引起了有關部門的極大關注。微觀交通仿真軟件是評估交通解決方案的有力工具，因此軟件模型的精確性就顯得至關重要。目前，我國大多數(shù)研究機構所使用的交通仿真軟件都從國外引進，因此依據(jù)我國實際交通運轉狀況對軟件模型參數(shù)進行校正是開展其余工作的前提和基礎。以往的參數(shù)校正算法大多采用遺傳算法，但是遺傳算法在迭代過程中會耗費大量的時間，同時目前大多數(shù)校正的方法都是獨立的程序，并未達成參數(shù)校正的自動化，為了解決上述問題，本文建立了基于改進方法的VISSIM參數(shù)自動校正體系。在研究過程中，主要實現(xiàn)了以下幾個方面的工作： 首先，，經(jīng)過對大量文獻的閱讀和整理，歸納出目前對于參數(shù)校正工作的研究正沿著兩條主線展開，而本文的研究重點也放在對于模型校正算法的研究上。本文以VISSIM仿真軟件為例，對軟件的核心模型——跟馳模型和換道模型的重要參數(shù)進行了詳細地說明，然后對參數(shù)校正過程中評價指標的選取和待校正參數(shù)的選取方法做了具體地介紹。 其次，本文以遺傳算法為參數(shù)校正方法，利用訓練好的廣義回歸神經(jīng)網(wǎng)絡模型預測仿真軟件VISSIM的輸出結果，這樣就避免了在遺傳算法迭代過程中需要反復運轉仿真軟件造成的時間浪費。這部分也是論文的核心之一。之后本文以北京市中關村一街為實例，對上述參數(shù)校正方法進行了實例驗證，結果證明，該方法能夠有效提高參數(shù)校正的效率，并且符合對模型精度的要求。 接著，文章建立了交通仿真軟件自動校正體系，達成了參數(shù)校正的流程化和自動化，用戶能夠經(jīng)過簡單的圖形界面達成對參數(shù)校正流程的控制，同時能夠獲得更加直觀的校正前后參數(shù)和評價指標的對照狀況。 最終，文章對上述建立的自動校正體系進行了實例的驗證，驗證的區(qū)域是江蘇省無錫市新區(qū)的主干道——菱湖大道從高浪路到震澤路路段，以平均行程時間為評價指標，以浮動車跟車和實地調查的方式對交通流數(shù)據(jù)進行了采集，并在VISSIM平臺上建立了仿真模型。運轉結果表明，用戶能夠經(jīng)過該體系達成對模型參數(shù)校正的空子，并且模型校正的結果在可接受的范圍內。
[Abstract]:With the rapid development of cities, the number of private cars is increasing year by year. Traffic congestion has aroused great concern of relevant departments in many cities. Microscopic traffic simulation software is a powerful tool for evaluating traffic solutions, so the accuracy of the software model is very important. At present, the traffic simulation software used by most research institutions in our country is imported from abroad, so it is the premise and foundation of the other work to correct the parameters of the software model according to the actual traffic operation in our country. In the past, most of the parameter correction algorithms used genetic algorithm, but the genetic algorithm in the iterative process will cost a lot of time, and most of the current correction methods are independent procedures, did not achieve the automation of parameter correction. In order to solve the above problems, an automatic correction system of VISSIM parameters based on the improved method is established. In the course of the research, the following aspects of the work are realized: First of all, through reading and sorting out a large number of documents, we conclude that the research of parameter correction is being carried out along two main lines, and the research emphasis of this paper is also on the research of model correction algorithm. Taking the VISSIM simulation software as an example, this paper gives a detailed description of the important parameters of the core model of the software, that is, the car-following model and the changing channel model. Then the selection of the evaluation index and the method of selecting the parameters to be corrected in the process of parameter correction are introduced in detail. Secondly, using the genetic algorithm as the parameter correction method, the trained generalized regression neural network model is used to predict the output of the simulation software VISSIM. In this way, the time waste caused by running simulation software repeatedly in the iterative process of genetic algorithm is avoided. This part is also one of the core of the paper. Then this paper takes Zhongguancun first Street in Beijing as an example to verify the above parameter correction method. The results show that the method can effectively improve the efficiency of parameter correction and meet the requirements of model accuracy. Then, the automatic correction system of traffic simulation software is established, and the process and automation of parameter correction are achieved. The user can control the process of parameter correction through a simple graphical interface. At the same time, the comparison of parameters and evaluation indexes before and after correction can be obtained more intuitively. Finally, the paper verifies the automatic correction system, which is the main road of Wuxi City, Jiangsu Province, from Gaolang Road to Zhenze Road, and takes the average travel time as the evaluation index. The traffic flow data were collected by floating vehicle following vehicle and field investigation, and the simulation model was established on VISSIM platform. The operation results show that the user can achieve the model parameter correction through the system, and the model correction results are acceptable.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U495;TP391.9

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本文編號：1803459