發(fā)布時(shí)間：2018-03-09 09:38

  本文選題：行為強(qiáng)化理論　切入點(diǎn)：誘導(dǎo)信息　出處：《重慶交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：本文主要圍繞出行者的路徑選擇、誘導(dǎo)信息的發(fā)布以及模型博弈平衡進(jìn)行了討論與研究。首先,論文將行為強(qiáng)化理論應(yīng)用到了駕駛員的路徑選擇行為中,提出了在有限理性條件下,駕駛員第k+1次的路徑選擇主要依賴于駕駛員自身第k次路徑選擇所獲得的收益,即:自學(xué)習(xí)機(jī)制。其次,論文將駕駛員的行程時(shí)間感受作為駕駛員的路徑選擇收益,并將其時(shí)間感受劃分成了三個(gè)模糊集,然后給出了各模糊集的隸屬度函數(shù)。最后,論文在有限理性和以時(shí)間感受為模糊集的前提下,建立了相應(yīng)的模型,并給出了各模型的求解算法與仿真驗(yàn)證。具體地來說,論文的主要研究工作內(nèi)容如下:①將操作反射條件理論應(yīng)用于駕駛員的車輛路徑選擇行為中,建立了基于有限理性模糊博弈無誘導(dǎo)信息條件下的車輛路徑選擇模型;證明了在模型的九種初始狀態(tài)下,該博弈最終會(huì)取得平衡,并給出了九種初始狀態(tài)下的博弈平衡結(jié)果。②建立了基于有限理性模糊博弈有誘導(dǎo)信息條件下的車輛路徑選擇模型,給出了該模型的求解算法,并采用具體算例對該模型進(jìn)行了仿真驗(yàn)證。③對比分析了有誘導(dǎo)信息條件下和無誘導(dǎo)信息條件下的博弈平衡結(jié)果,結(jié)果顯示:誘導(dǎo)信息的發(fā)布并不是一直有效的,誘導(dǎo)信息的有效性與參與博弈的局中人總數(shù)以及初始交通流的分布有密切關(guān)系;當(dāng)參與博弈局中人總數(shù)小于或接近路網(wǎng)總通行能力時(shí),發(fā)布誘導(dǎo)信息對減小局中人總行程時(shí)間是有利的,且路徑L1的初始流量所占比例越接近C1/(C1+C2),誘導(dǎo)信息的有效性越差;誘導(dǎo)信息的有效性隨路網(wǎng)總流量的增加而增加,當(dāng)參與博弈局中人總數(shù)遠(yuǎn)大于路網(wǎng)總通行能力時(shí),發(fā)布誘導(dǎo)信息能有效減小局中人總行程時(shí)間。④建立了累積自學(xué)習(xí)機(jī)制的車輛路徑選擇模型,給出了模型的求解算法,并采用具體算例進(jìn)行了仿真驗(yàn)證,仿真結(jié)果表明:在累積自學(xué)習(xí)機(jī)制下,無誘導(dǎo)信息的車輛路徑選擇博弈結(jié)果與發(fā)布誘導(dǎo)信息的車輛路徑選擇博弈結(jié)果無顯著差異。⑤分析了有誘導(dǎo)信息條件下的博弈模型參數(shù)ζ的變化對博弈平衡結(jié)果的影響。論文采用仿真驗(yàn)證的方法分析ζ的變化對博弈平衡結(jié)果的影響,仿真結(jié)果表明:當(dāng)參與博弈車輛總數(shù)少于路網(wǎng)總通行能力時(shí),ζ的變化對博弈平衡結(jié)果影響顯著;當(dāng)參與博弈車輛總數(shù)遠(yuǎn)大于路網(wǎng)總通行能力時(shí),ζ的變化對博弈平衡結(jié)果無影響;在參與博弈車輛總數(shù)一定的情形下,初始狀態(tài)接受誘導(dǎo)的車輛比例越大,ζ的變化對博弈平衡結(jié)果的影響越小。⑥最后論文分析討論了博弈模型參數(shù)ζ的變化對誘導(dǎo)效果的影響。結(jié)果表明:ζ能影響局中人總行程時(shí)間T總,它對T總的影響顯著性與博弈的初始接受誘導(dǎo)的車輛比例及博弈局中人總數(shù)相關(guān);當(dāng)局中人總數(shù)小于(或接近)路網(wǎng)總通行能力時(shí),ζ對T總的影響主要與路網(wǎng)流量的初始接受誘導(dǎo)的車輛比例有關(guān);當(dāng)局中人總數(shù)遠(yuǎn)大于路網(wǎng)總通行能力時(shí),ζ對T總影響不顯著。
[Abstract]:This paper mainly focuses on the travelerroute choice, information release and induced model of game equilibrium are discussed and studied. Firstly, the behavior reinforcement theory is applied to the driver's route choice behavior, proposed in the limited rationality, driver path selection in k+1 mainly bases on the driver's own K path selection of gains, namely: self-learning mechanism. Secondly, the travel time of driver's feelings as the driver's route choice and the time to feel benefits, divided into three fuzzy sets, and then gives the fuzzy set membership function. Finally, in the premise of limited rationality and time to feel for fuzzy sets, established the corresponding model, and gives the algorithm and Simulation of the validation of the model. Specifically, the main contents are as follows: 1. The operation of reflection The vehicle routing application conditions to the driver's choice behavior theory, established the finite rational fuzzy game by vehicle route choice model based on information conditions; proved that nine kinds of initial state model, the game will eventually achieve a balance, and the balance state of the game results was nine at the beginning of the establishment are given. The limited rationality of fuzzy game induced by vehicle path under the condition of information selection model based on the algorithm of the model is given, and the example for the simulation of the model. The comparison and analysis of the induction information and by game equilibrium results, under the conditions of information showed that the induction and release the information is not always effective, there is a close relationship between the effectiveness of the guidance information bureau and in the total number of people involved in the game and the initial traffic flow distribution; when the total number of players in the game Less than or close to the total network capacity, issued guidance information is favorable to the reduction in the total travel time of most people, and the initial flow path L1 proportion is close to C1/ (C1+C2), the effectiveness of the guidance information is poor; the effectiveness of the guidance information increases with the increase of the total network traffic, when in the total number of game players is far greater than the total network capacity, can effectively reduce the induced release of small players total travel time information. The model utilizes the vehicle routing self-learning mechanism selection model, gives the algorithm to solve the model, and the example was simulated, simulation results show that the accumulation of self learning mechanism, no vehicle route guidance information selection results of the game with the release of vehicle route guidance information selection game results have no significant difference. 5. The induced game model parameters under the condition of the change of zeta information on Effect of game equilibrium results. The method changes the simulation analysis on the game of the zeta balance results, simulation results show that when the total number of vehicles in the game less than the total network capacity, the impact of change on the balance of the game. In the game when the total number of vehicles significantly; far greater than the total network capacity, zeta the change has no effect on game equilibrium results; the total number of vehicles in the game in certain circumstances, the initial state of the vehicle induced greater proportion of acceptance, the change of zeta balance results is small. The paper finally analyzes the influences of model parameters on the change of the game zeta induction effects discussed. The results show that the zeta can the people in the game affect the total travel time of T, its impact on the overall T initial significant and game total vehicle proportion and accept by game players; the total number of people in authority (or close to less than ) the total network capacity, the main effect of T zeta total with the initial network flow induced by the vehicle accept proportion; total authorities people far greater than the total network capacity, there was no significant difference on the total effect of zeta T.

【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495

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本文編號(hào)：1587977