AI 中文总结
该研究提出网络上非局部交通流模型的通用框架，结合守恒定律与非局部速度函数，通过缓冲区耦合路口动力学。以动态k最短路径路由为例，还可优化路由，形成交通演变与路线选择的反馈回路，数值示例验证了方法的稳健性。
AI 中文摘要
我们提出了一个用于具有多种商品的网络上宏观非局部交通流模型的通用计算框架。该模型将边上的标量守恒定律与非局部速度函数相结合，并通过基于缓冲区的路口动力学进行耦合。路由一般定义为在出路段上分配驾驶员的规则。例如，我们实现动态k最短路径路由，利用道路行驶时间和路口等待时间计算每个时间步到商品目的地的最短路径并相应分配驾驶员。还通过优化时间范围内的路由来最小化总行驶时间。此框架在交通演变和路线选择之间自然形成反馈回路。数值示例展示了该方法的稳健性并可比较不同路由策略。
英文摘要
We present a general computational framework for macroscopic nonlocal traffic flow models on networks with multiple commodities. The model combines scalar conservation laws on edges with nonlocal velocity functions and couples them via buffer-based junction dynamics. Routing is defined in general as a prescription for distributing drivers across outgoing road segments. As an example, we implement dynamic k-shortest-path routing, where travel times along roads and waiting times at intersections are used to compute shortest paths to the commodities' destinations at each time step, and drivers are distributed accordingly. In another example, we optimize routing over a considered time horizon to minimize the total travel time. This framework naturally creates a feedback loop between traffic evolution and route choice. Numerical examples, ranging from small test cases to large grid-like networks, demonstrate the robustness of the approach and allow for a comparison of different routing strategies.
Comments20 pages, 10 figures
