论文标题

一种避免在拥挤的链接上避免虚幻高流量并调查网络拥堵的演变的方法

An Approach to Avoid the Unreal High Flows on Congested Links and Investigates the Evolution of Congestion over Network

论文作者

He, Shengxue

论文摘要

当在流量分配中采用流量量的单调增加的链路旅行时间函数时,在结果中,虚幻的高流量可能会出现在实际拥挤的链接上。静态交通分配模型(TAM)的固定链路流量结果几乎不可能在预定的观测期内调查和利用拥挤区域在网络上的实际演变。已经提出了许多方法,例如带有链路流量的侧面约束和基于日常流量的伪动态流量分配,以提高TAM结果的可靠性,但无法消除问题。为了解决上述问题,我们首先通过分析链接旅行时间函数和交通流理理论的基本图之间的联系来揭示问题的起源。根据上述连接,制定了映射以反映基本图的两个分支。然后假设给出了链路流量状态,则提出了新的流量分配模型。为了解决获得的非凸用户平衡模型,分支和结合算法的设计基于目标函数的非线性部分的线性化。最后,通过反复解决相应的交通分配模型,具有链接的初始流量状态,可以复制和研究在观察期间的拥挤区域的演变。数值示例证明了新方法的有效性。

The unreal high flows may appear on the actually congested links in the result when a monotonically increasing link travel time function of flow volume is adopted in traffic assignment. The fixed link flow results of a static traffic assignment model (TAM) make it nearly impossible to investigate and make use of the actual evolution of congested zones over the network during the predetermined observation time period. Many methods, such as TAMs with side-constraints on link flow capacity and the pseudo dynamic traffic assignment based on day-by-day traffic, have been proposed to improve the reliability of the results of TAM, but cannot eliminate the problem. To resolve the above problems, we first uncover the origin of problem by analyzing the connection between the link travel time function and the fundamental diagram of traffic flow theory. According to the above connection a mapping is formulated to reflect the two branches of the fundamental diagram. Then with the assumption that the link flow states are given, new traffic assignment models are presented. To resolve the obtained non-convex user equilibrium model, a branch-and-bound algorithm is designed based on linearizing the nonlinear part of the objective function. At last, by repeatedly resolving the corresponding traffic assignment model with the renewed initial flow states of links, the evolution of congested zones during the observation time period can be reproduced and investigated. The numerical examples demonstrate the effectiveness of the new approach.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源