Modeling stochastic perception error in the mean-excess traffic equilibrium model
Modeling stochastic perception error in the mean-excess traffic equilibrium model
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DOI：
10.1016/j.trb.2011.05.028
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发表时间：
2011-12
影响因子：
6.8
通讯作者：
A. Chen;Zhong Zhou;W. Lam
中科院分区：
文献类型：
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作者：
A. Chen;Zhong Zhou;W. Lam
本文扩展了Chen和Zhou的α-可靠平均超额交通平衡（METE）模型（交通研究B部分44(4),2010,493-513），明确建模了出行者路线选择决策过程中的随机感知误差。在METE模型中，每个出行者不仅在置信水平α上考虑确保准时到达的旅行时间预算，而且还考虑了在分布尾部（1−α）分位数上遇到更差旅行时间的影响。此外，由于对旅行时间变化的认识不完善，特别是在没有先进的出行者信息系统的拥挤网络中，出行者的路线选择决策是基于感知的旅行时间分布而不是实际的旅行时间分布。为了计算感知平均超额走时，提出了一种基于矩分析的近似方法。它涉及到使用条件矩生成函数来导出感知的路段旅行时间，使用Cornish-Fisher渐近展开来估计感知的旅行时间预算，以及使用Acerbi和Tasche近似来估计感知的平均超额旅行时间。本文提出的随机平均超额交通平衡（SMETE）模型被表述为一个变分不等式（VI）问题，并采用改进的交替方向法求解基于路径的求解算法。数值算例说明了所提出的SMETE模型和求解方法的应用。
In this paper, we extend the α-reliable mean-excess traffic equilibrium (METE) model of Chen and Zhou (Transportation Research Part B 44(4), 2010, 493–513) by explicitly modeling the stochastic perception errors within the travelers’ route choice decision processes. In the METE model, each traveler not only considers a travel time budget for ensuring on-time arrival at a confidence level α, but also accounts for the impact of encountering worse travel times in the (1−α) quantile of the distribution tail. Furthermore, due to the imperfect knowledge of the travel time variability particularly in congested networks without advanced traveler information systems, the travelers’ route choice decisions are based on the perceived travel time distribution rather than the actual travel time distribution. In order to compute the perceived mean-excess travel time, an approximation method based on moment analysis is developed. It involves using the conditional moment generation function to derive the perceived link travel time, the Cornish–Fisher Asymptotic Expansion to estimate the perceived travel time budget, and the Acerbi and Tasche Approximation to estimate the perceived mean-excess travel time. The proposed stochastic mean-excess traffic equilibrium (SMETE) model is formulated as a variational inequality (VI) problem, and solved by a route-based solution algorithm with the use of the modified alternating direction method. Numerical examples are also provided to illustrate the application of the proposed SMETE model and solution method.