A Macro-Micro Mixed Traffic Flow Models based on Cell Transmission Models and Cellular Automaton
|Keywords:||格位傳遞模式;細胞自動機;巨微觀轉換;混合車流;Cell transmission model;cellular automaton;macro-micro traffic flow model;mixed traffic|
|Abstract:||巨觀車流模式係計算道路上整體車流之流量、密度與速率間關係，運作優點在於模擬效率高；微觀車流模式則處理個別車輛間較為細緻之車流行為，針對車間距、速差等刺激項對應做出加減速反應，優點在於資料準確度高，當各取其優點整合二種模式進行模擬運作時，須面臨效率與精確度之權衡(trade-off)。本研究以市區道路汽車與機車混合車流為背景，以謝志偉(2010)所提出混合車流格位傳遞模式(Mixed Traffic Cell Transmission Models，MCTM)為基礎，模擬車隊在路段格位間整體車流傳遞之行為，降低模擬時間；再以Lan et al.(2009、2010)細胞自動機(Cellular Automaton，CA)以及提出變換車道決策之概念為基礎，模擬車隊於鄰近路口混合併行與變換車道等行為。為使前述兩模式能整合運作，設計巨觀與微觀模式資訊傳遞轉換介面，結合兩模式之優點並考慮混合車流特性構建巨微觀混合車流模式。
The macroscopic traffic flow models which account for the flow rate, density or speed of groups of vehicles on the roads, are advantageous to its high efficiency. On the contrary, the microscopic traffic flow models deal with the stimulation terms to spacing or relative speed between individual vehicles and respond to its acceleration or deceleration. The advantage of them lie in describing detailed vehicle behaviors and high accuracy. Therefore, it is necessary to be confronted with a trade-off between efficiency and accuracy when conducting mixed traffic flow models simulation. This study takes the prevailing mixed traffic of cars and motorcycles on urban streets as the background. Firstly, based on the mixed traffic cell transmission models (MCTM), proposed by C.W. Hsieh (2010), to simulate platoons of vehicles with traffic flow transference within segment cells to reduce simulation time. Secondly, based on the cellular automaton and microscopic lane changing principles in mixed traffic, proposed by Lan et al.(2009、2010), to simulate platoons of vehicles with parallel driving and lane changing behaviors in close proximity to the intersection. In order to make these two models stated above integrate properly, this study develops a macro-micro traffic flow models with a transmission interface design, which combines advantages over both models and takes characteristics of mixed traffic into consideration. Moreover, real data of mixed traffic on urban streets is collected. To obtain all the vehicle trajectories in the experimental segment and calculate numbers of vehicles in each cell at each time step through analyzing them. The proposed model in different scenarios is assumed and divided into seven kinds of macro-micro proportions. The efficiency index is measured by CPU time and accuracy index is by SMAPE values to validate the proposed model. This study puts emphasis on the simulation effectiveness of interfaces in different positions of the segment and tries to find out the best one. The results demonstrate that the macroscopic traffic flow models is conductive to reduce the simulation time and improve efficiency. The microscopic traffic flow models make for accuracy. Under a balanced of efficiency and accuracy, the best position of the interface measured by simulation effectiveness is thirty to sixty meters apart from the intersection. It is supposed to transfer the model from macroscopic to microscopic before the traffic flow is interrupted.
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