A trip-chain-based stochastic dynamic transportation assignment model is proposed. In this model, travelers determine their route to minimize the total trip-chain travel cost. Based on mesoscopic traffic simulator, coupled with the time dependent K shortest path and the method of successive average, a solution algorithm is developed. A case is made to illustrate the application of the proposed model and algorithm by contrasting it to current practice and results show that current practice overestimates network performance.%提出了一个基于出行链的动态随机交通分配模型.在这个模型里,出行者选择路径以最小化他们整个出行链上的感知出行成本.基于中观的交通/活动仿真器,结合时变的K最短路算法和连续平均法,设计了模型的求解算法.通过一个算例,对基于出行链的交通分配模型进行了分析,并与传统的基于出行的交通分配模型进行了比较,结果表明,传统分配模型忽略停驻点时,所求的平均出行时间要小于实际考虑出行链时的平均出行时间.
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