A Cell‐Based Model for Multi‐class Doubly Stochastic Dynamic Traffic Assignment
A Cell‐Based Model for Multi‐class Doubly Stochastic Dynamic Traffic Assignment
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DOI：
10.1111/j.1467-8667.2011.00717.x
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
W. Y. Szeto;Yu Jiang;A. Sumalee
中科院分区：
文献类型：
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作者：
W. Y. Szeto;Yu Jiang;A. Sumalee
摘要：提出了一种考虑交通状态随机演化的基于单元的多类动态交通分配问题。假设出行者根据感知有效旅行时间选择路线，其中有效旅行时间为平均旅行时间与安全裕度之和。提出的问题被表述为一个不动点问题，其中包括一个基于蒙特卡罗的随机细胞传输模型，以捕获物理队列的影响和流量传播过程中交通状态的随机演变。用自调节平均法解决了不动点问题。结果说明了问题的性质和求解方法的有效性。主要调查结果如下：(1)减少对交通状况的感知误差可能无法减少估计系统性能的不确定性；(2)与使用连续平均方法相比，使用自调节平均方法在大多数测试用例中可以给出更快的收敛速度；(3)步长参数值的组合对收敛速度有很大影响；(4)更高的需求，更好的信息质量。或者驾驶员风险规避程度越高，计算时间越长；(5)驾驶员类别越多，计算时间不一定越长；(6)在求解过程的早期阶段，采用小样本量可以显著减少计算时间。
Abstract: This article proposes a cell‐based multi‐class dynamic traffic assignment problem that considers the random evolution of traffic states. Travelers are assumed to select routes based on perceived effective travel time, where effective travel time is the sum of mean travel time and safety margin. The proposed problem is formulated as a fixed point problem, which includes a Monte–Carlo‐based stochastic cell transmission model to capture the effect of physical queues and the random evolution of traffic states during flow propagation. The fixed point problem is solved by the self‐regulated averaging method. The results illustrate the properties of the problem and the effectiveness of the solution method. The key findings include the following: (1) Reducing perception errors on traffic conditions may not be able to reduce the uncertainty of estimating system performance, (2) Using the self‐regulated averaging method can give a much faster rate of convergence in most test cases compared with using the method of successive averages, (3) The combination of the values of the step size parameters highly affects the speed of convergence, (4) A higher demand, a better information quality, or a higher degree of the risk aversion of drivers can lead to a higher computation time, (5) More driver classes do not necessarily result in a longer computation time, and (6) Computation time can be significantly reduced by using small sample sizes in the early stage of solution processes.