本文選題：交通仿真　切入點(diǎn)：VISSIM　出處：《首都經(jīng)濟(jì)貿(mào)易大學(xué)》2015年碩士論文

【摘要】：城市快速路的修建主要是為了解決交通需求的高速增長(zhǎng),在其與相交道路連接的必要過(guò)渡處即出入口,卻成為造成城市快速路擁堵的一大原因。本文采用微觀交通仿真方法,解決這一關(guān)鍵問(wèn)題。交通仿真是模擬現(xiàn)實(shí)交通狀況分析交通狀態(tài)的有效方法,目前我國(guó)使用的交通仿真軟件大多由國(guó)外研發(fā),其模型參數(shù)設(shè)置不適于我國(guó)的交通流特性。因此本文將從模型參數(shù)校正出發(fā),結(jié)合群智能優(yōu)化算法的改進(jìn)研究開發(fā)一個(gè)高效準(zhǔn)確的參數(shù)校正程序,應(yīng)用于城市快速路出入口仿真中。首先,對(duì)人工蜂群算法進(jìn)行改進(jìn)研究。人工蜂群算法(ABC)是模擬蜜蜂采蜜行為的群智能優(yōu)化算法,針對(duì)其容易陷入早熟收斂等不足,引入文化算法中的雙層進(jìn)化結(jié)構(gòu)和多種群并行進(jìn)化思想,提出基于雙層進(jìn)化的多種群并行人工蜂群算法(PMABC)。將采蜜蜂群劃分為具有不同搜索策略的子種群并行進(jìn)化,平衡全局開發(fā)能力與局部搜索能力,避免算法過(guò)早陷入局部最優(yōu)。采用雙層進(jìn)化結(jié)構(gòu),采蜜蜂群作為種群空間尋找可行解,追隨蜂群作為信仰空間,記憶采蜜蜂群搜索的優(yōu)質(zhì)蜜源并繼續(xù)搜索,其搜索結(jié)果用于指導(dǎo)蜂群尋優(yōu),加速算法收斂,提高收斂精度。通過(guò)六個(gè)經(jīng)典的適應(yīng)度測(cè)試函數(shù)仿真實(shí)驗(yàn)表明,該算法能夠有效避免算法陷入局部最優(yōu)并具有較快的收斂速度和較高的收斂精度。其次,結(jié)合VISSIM使用手冊(cè)介紹VISSIM軟件特點(diǎn),說(shuō)明選擇其作為本次校正及仿真工具的原因。針對(duì)主輔路出入口這一研究對(duì)象,確定需要校正的參數(shù)和參數(shù)校正的評(píng)價(jià)指標(biāo),選擇北京三環(huán)光華橋附近的出入口進(jìn)行數(shù)據(jù)采集,建立仿真模型。通過(guò)COM接口使用VBA語(yǔ)言開發(fā)一套自動(dòng)校正程序,該程序所使用的優(yōu)化算法為多種群并行人工蜂群算法。最后,分析了校正結(jié)果,從而證明了本文所提出的改進(jìn)的群智能優(yōu)化算法和自動(dòng)校正程序是有效的,極大的提高了模型的準(zhǔn)確性,且多種群并行人工蜂群算法的運(yùn)用提高了校正的速度。該校正程序可應(yīng)用于各個(gè)微觀交通仿真中,具有普適性,對(duì)于我國(guó)交通仿真的研究與應(yīng)用具有重要意義。
[Abstract]:The construction of urban expressway is mainly to solve the rapid growth of traffic demand. The entrance and exit, which are necessary to connect with the intersecting road, have become a major cause of urban expressway congestion. This paper adopts microscopic traffic simulation method. Traffic simulation is an effective method to simulate the real traffic situation and analyze the traffic state. At present, most of the traffic simulation software used in our country are developed by foreign countries. The model parameter setting is not suitable for the traffic flow characteristics in China, so this paper will develop an efficient and accurate parameter correction program based on the model parameter correction and the improvement of the swarm intelligence optimization algorithm. First of all, the artificial bee colony algorithm is improved. The artificial bee colony algorithm (ABC) is a swarm intelligence optimization algorithm to simulate honeybee honey gathering behavior, which is easy to fall into premature convergence and other shortcomings. By introducing the two-layer evolutionary structure in the cultural algorithm and the idea of multi-population parallel evolution, a multi-population parallel artificial swarm algorithm based on double-layer evolution is proposed. The honeybee colony is divided into sub-populations with different search strategies. The global development ability and the local search ability are balanced to avoid the algorithm falling into local optimum prematurely. The two-layer evolutionary structure is adopted to find the feasible solution in the population space of honeycomb, and to follow the colony as the belief space. The high quality honey source of honeybee colony search is memorized and continued. The search results are used to guide the colony optimization, accelerate the convergence of the algorithm and improve the convergence accuracy. The simulation results of six classical fitness test functions show that, The algorithm can effectively avoid falling into local optimum, and has faster convergence speed and higher convergence precision. Secondly, the characteristics of VISSIM software are introduced with the help of VISSIM manual. Explain the reason why it is chosen as the tool of correction and simulation. In view of the main and auxiliary road entrance and exit, determine the parameters that need correction and the evaluation index of parameter correction, and select the entrance and exit near Beijing Sanhuan Guanghua Bridge to collect data. The simulation model is established. A set of automatic correction program is developed by using VBA language through COM interface. The optimization algorithm used in this program is multi-colony parallel artificial colony algorithm. Finally, the correction results are analyzed. It is proved that the improved swarm intelligence optimization algorithm and the automatic correction program proposed in this paper are effective and greatly improve the accuracy of the model. The application of multiple swarm parallel artificial bee swarm algorithm improves the speed of correction. This program can be used in various microscopic traffic simulation, which is universal and has great significance for the research and application of traffic simulation in our country.
【學(xué)位授予單位】：首都經(jīng)濟(jì)貿(mào)易大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U491

【參考文獻(xiàn)】

相關(guān)會(huì)議論文 前1條

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本文編號(hào)：1679518