【摘要】 本文研究了可交易路票策略(Tradable Credit Scheme, TCS)下双模式随机网络中的交通均衡配流问题。采用累积前景理论(Cumulative Prospect Theory, CPT)来描述出行者在不确定环境下的风险决策行为。假设出行者选择理解的广义路径费用(包括时间前景值和货币费用)最小的路径进行出行。在给定路票策略下的交通均衡状态,内生的参考点和路票价格保持不变,而且与道路子网络中的均衡流量形态和对应的出行时间概率分布一致。为了描述这种交通均衡状态,本文构建了路票策略下基于CPT的随机用户均衡(Stochastic User Equilibrium,SUE)条件。然后,建立了一个嵌套参数型不动点(Fixed Point,FP)模型的等价变分不等式(Variational Inequality,VI)模型,并在理论上分析了该模型的相关特性。本文设计了一种启发式算法来求解该模型,该算法包含两层迭代过程。其中,外层迭代是一个基于二分的收缩算法,用于寻找均衡路票价格;内层迭代本质上是相继平均算法(Method of Successive Averages,MSA),用于确定对应的基于CPT的SUE网络流量形态。通过数值实验,本文验证了模型和算法的正确性和有效性。更多还原

【Abstract】 The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.更多还原