诱导信息下考虑路段容量退化的流量演化研究

Day-to-day travel choice dynamics with guidance information in degradable transport network

在路段通行能力随机退化的情况下,假设出行者路径选择行为随出行经验和出行可靠性信息的更新而不断调整.考虑出行者择路过程中的有限理性和参考点依赖,基于累积前景理论的路径选择决策机制,建立了描述出行者动态学习、经验更新、预留出行时间更新和动态选择的交通流day-to-day动态演化模型,定义了广义的交通流系统收敛并提出模型的计算算法.通过算例解释上述模型和算法,经过大规模仿真计算和比较,发现在随机情境下提供出行可靠性诱导信息,信息准确程度较高时网络可以较快演化到收敛状态.本文的研究有助于加深对复杂交通行为的理解,对交通规划和管理具有理论指导意义.

It is assumed that travelers adjust route choices according to updated experience and guidance information from the Reliable Path Searching System in the form of a travel time budget in degradable transport network. With the consideration of bounded rationality and reference dependency,we develop a descriptive dayto-day dynamic model of network flow in the framework of Cumulative Prospect Theory（ CPT）. This model reveals how travelers learn,update and adjust their travel time budgets as well as route choices from day-to-day.The properties of the day-to-day dynamic model are then discussed,and a solution algorithm is proposed to solve the model. We then conduct numerical examples to illustrate its properties,and it was demonstrated that the day-to-day dynamics can quickly evolve to be convergent when the guidance information is relatively accurate（ with smaller prediction error）. Furthermore,the convergence state of the day-to-day dynamic model is approximately identical to Wardrop user equilibrium. Such an understanding of complex travel behaviour has important implications on transportation planning and management.