A Proposed Framework for Simultaneous Optimization of Evacuation Traffic Distribution and Assignment
A Proposed Framework for Simultaneous Optimization of Evacuation Traffic Distribution and Assignment
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发表时间：
2005
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通讯作者：
Fang Yuan
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作者：
Fang Yuan
在传统的疏散规划过程中，疏散人员被分配到固定的目的地，主要是基于地理接近的标准。然而，这种预先指定的目的地（OD表）几乎总是导致次优的疏散效率，由于不确定的道路条件，如拥堵，道路堵塞，和其他与紧急情况相关的危险。单目的地疏散（ODE）概念通过放松对疏散人员分配到预定目的地的约束，具有极大提高疏散效率的潜力。因此，在这项研究中提出了一个框架，同时优化疏散交通分配和分配。基于常微分方程的概念，最优的目的地和路径分配可以通过解决一个单一的目的地（1D）交通分配问题的修改后的网络表示。当在真实网络上进行疏散研究时，所提出的1D模型比传统的多目的地（nD）模型有了很大的改进。例如，对于一个假设的全县范围内的疏散，在1D框架中的交通路线与途中信息建模时，可以实现近80%的整体疏散时间减少，1D优化结果也可以用于改善规划OD表，导致高达60%的整体疏散时间减少。更重要的是，该框架可以实际实施，并且其效率提高可以简单地通过指示疏散人员前往根据预先执行的1D优化确定的更高效的目的地来实现。
In the conventional evacuation planning process, evacuees are assigned to fixed destinations based mainly on the criterion of geographical proximity. However, such prespecified destinations (OD table) almost always lead to sub-optimal evacuation efficiencies due to uncertain road conditions such as congestion, road blockage, and other hazards associated with the emergency. By relaxing the constraint of assigning evacuees to pre-specified destinations, a one-destination evacuation (ODE) concept has the potential of greatly improving the evacuation efficiency. A framework for simultaneous optimization of evacuation traffic distribution and assignment is therefore proposed in this study. Based on the concept of ODE, the optimal destination and route assignment can be determined by solving a one-destination (1D) traffic assignment problem on a modified network representation. When tested on real-world networks for evacuation studies, the proposed 1D model presents substantial improvement over the conventional multiple-destination (nD) model. For instance, for a hypothetical county-wide evacuation, a nearly 80% reduction in the overall evacuation time can be achieved when modeling of traffic routing with en route information in the 1D framework, and the 1D optimization results can also be used to improve the planning OD tables, resulting in an up to 60% reduction in the overall evacuation time. More importantly, this framework can be actually implemented, and its efficiency enhancement can be realized simply by instructing evacuees to head for more efficient destinations determined from the 1D optimization performed beforehand.