【摘要】：論文針對現(xiàn)有研究不足,借助行為動力學(xué)建模、數(shù)值模擬與數(shù)學(xué)優(yōu)化理論等方法,從日變交通路徑調(diào)整模型、調(diào)整算法與交通信息發(fā)布策略三個層面系統(tǒng)地展開研究。論文的主要創(chuàng)新工作總結(jié)如下： (1)通過指證經(jīng)典比例調(diào)整過程的兩個行為不足(即弱魯棒性與過度調(diào)整問題),建立了基于對向行為準(zhǔn)則的非線性對向調(diào)整動力學(xué)(NPSD)模型,然后以此為基礎(chǔ),進(jìn)一步探討了受多日出行經(jīng)歷影響、有限理性情形及最短路導(dǎo)向行為準(zhǔn)則下的交通路徑調(diào)整行為,分別建立了多日NPSD (MNPSD)模型、有限理性NPSD (BRNPSD)模型以及最短路導(dǎo)向的非線性調(diào)整動力學(xué)(NMSD)模型。研究發(fā)現(xiàn)：i)以上四種路徑調(diào)整模型均能避免弱魯棒性與過度調(diào)整問題,進(jìn)而維持迭代解集不變,同時各自的穩(wěn)定路徑流模式與其對應(yīng)的用戶均衡等價；ii) NPSD與NMSD均為理性行為調(diào)整過程,并且二者的連續(xù)型模型是全局穩(wěn)定的。數(shù)值結(jié)果表明：i)用戶反應(yīng)靈敏度對網(wǎng)絡(luò)交通流演化過程與結(jié)果具有重要影響；ii)路徑調(diào)整中過分依賴過往出行經(jīng)驗(yàn)反而可能增加網(wǎng)絡(luò)的不穩(wěn)定風(fēng)險；iii)有限理性下的網(wǎng)絡(luò)演化穩(wěn)定性并非總比完美理性強(qiáng)；iv) DNPSD的穩(wěn)定性優(yōu)于DNMSD,并且前者更適用于開發(fā)交通流均衡求解算法。 (2)通過引入風(fēng)險傾向型路徑旅行時間測度MBTT,并將其與風(fēng)險規(guī)避型測度METT進(jìn)行凸組合,構(gòu)建了可描述各種風(fēng)險態(tài)度的路徑旅行時間測度CMTT,再將其納入旅行時間可靠性測度(RM)框架,提出了基于RM的NPSD (RMNPSD)模型,再將反應(yīng)靈敏度回溯算子引入其中,提出了求解可靠性用戶均衡(RMUE)的非線性對向調(diào)整算法(NPSA). NPSA無需參數(shù)可行性試錯過程,且不依賴于導(dǎo)數(shù)也無需搜索迭代方向與步長。數(shù)值結(jié)果顯示NPSA的計算效率較高、可作為其他局部收斂算法的初始點(diǎn)搜索方法。 (3)基于出行者的有限感知記憶能力假設(shè),提出并建立了日變事后交通信息的適度矯正發(fā)布策略及其動態(tài)規(guī)劃模型,鑒于模型目標(biāo)函數(shù)非線性、連續(xù)不光滑且無封閉的顯式表達(dá)式,論文視其為一個靜態(tài)非線性規(guī)劃,并提出一種改進(jìn)型羅盤搜索算法對其進(jìn)行求解�；诤唵维F(xiàn)實(shí)路網(wǎng)的數(shù)值模擬結(jié)果表明事后信息矯正發(fā)布策略可提升網(wǎng)絡(luò)運(yùn)行效率,并且改進(jìn)型羅盤搜索具有良好的尋優(yōu)能力。
[Abstract]:Aiming at the deficiency of the existing research, this paper systematically studies the daily variable traffic path adjustment model, the adjustment algorithm and the traffic information release strategy with the help of behavioral dynamics modeling, numerical simulation and mathematical optimization theory. The main innovations of this paper are summarized as follows: (1) by pointing out the two shortcomings of the classical proportional adjustment process (that is, weak robustness and excessive adjustment), the nonlinear counter-direction behavior based on the counter-directional behavior criterion is established. (NPSD) model of adjusting dynamics, On the basis of this, this paper further discusses the traffic path adjustment behavior under the influence of multi-day travel experience, limited rationality and shortest path guidance behavior, and establishes the multi-day NPSD (MNPSD) model, respectively. The finite rational NPSD (BRNPSD) model and the shortest path oriented nonlinear adjusting dynamics (NMSD) model. It is found that the above four path adjustment models can avoid the problem of weak robustness and excessive adjustment, and then keep the iterative solution set unchanged, and their stable path flow patterns are equivalent to their corresponding user equilibrium. Both ii) NPSD and NMSD are rational behavior adjustment processes, and their continuous models are globally stable. The numerical results show that the sensitivity of user response has an important influence on the evolution process and result of network traffic flow. II) the excessive reliance on past travel experience in path adjustment may increase the risk of network instability. Iii) the evolutionary stability of the network is not always better than that of perfect rationality. The stability of iv) DNPSD is better than that of DNMSD, and the former is more suitable for developing traffic flow equilibrium algorithm. (2) by introducing the risk-based path travel time measure MBTT, and combining it with the risk-averse measure METT, A path travel time measure (CMTT,), which can describe various risk attitudes, is constructed and incorporated into the travel time reliability measure (RM) framework. The NPSD (RMNPSD) model based on RM is proposed, and the reaction sensitivity traceback operator is introduced into it. In this paper, a nonlinear counterbalancing algorithm, (NPSA)., is proposed to solve the reliability user equilibrium (RMUE). NPSA does not need parameter feasibility trial and error process, and does not depend on derivative, and does not need to search the direction and step of iteration. Numerical results show that NPSA is more efficient and can be used as an initial point search method for other local convergence algorithms. (3) based on the assumption of limited perceptual memory ability of travelers, In this paper, an appropriate corrective release strategy and its dynamic programming model for traffic information after daily change are proposed and established. In view of the nonlinear objective function of the model, the explicit expression of continuous non-smooth and unclosed, the paper regards it as a static nonlinear programming. An improved compass search algorithm is proposed to solve the problem. The numerical simulation results based on the simple reality road network show that the post-event information correction and release strategy can improve the efficiency of the network and the improved compass search has a good ability to search for optimal results.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：U491

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