Traffic bottlenecks dynamically change with the variance of traffic demand. Identifying traffic bottlenecks plays an important role in traffic planning and provides decision making. However, traffic bottlenecks are difficult to identify because of the complexity of traffic road networks and many other factors. In this article, we propose an influence spreading based method to find the dynamic changed traffic bottlenecks, where the influence caused by bottlenecks is maximal. We first build a traffic congestion diffusion (TCD) model to capture traffic flow influence (TFI) spreading over traffic road networks. The bottlenecks identification problem based on TCD is modeled as an influence maximization problem, that is, selecting the most influential nodes such that the deterioration of traffic condition is maximal. With the proof of the submodularity of TFI spreading over traffic networks, a provably near‐optimal algorithm is used to solve the NP‐hard problem. With the exploration of unique properties of TFI spread, an approximate influence maximization method for TCD (TCD‐AIM) is proposed. To the best of our knowledge, this should be the first model for a metro‐city scale from the influence perspective. Experimental results show that TCD‐AIM finds bottlenecks with up to 130% congestion density increase in the future.

中文翻译：

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基于拥挤扩散模型的都市规模影响最大化动态交通瓶颈识别

流量瓶颈会随着流量需求的变化而动态变化。识别交通瓶颈在交通规划中起着重要作用，并提供决策依据。然而，由于交通路网的复杂性和许多其他因素，交通瓶颈难以识别。在本文中，我们提出了一种基于影响力扩散的方法来查找动态变化的交通瓶颈，其中瓶颈引起的影响最大。我们首先建立交通拥堵扩散（TCD）模型，以捕获在交通道路网络上扩散的交通流影响（TFI）。将基于TCD的瓶颈识别问题建模为影响最大化问题，即选择影响最大的节点，以使交通状况的恶化最大。通过证明TFI的子模量分布在交通网络上，使用了一种可证明的近优算法来解决NP难题。通过探索TFI传播的独特特性，提出了TCD的近似影响最大化方法（TCD-AIM）。就我们所知，从影响角度来看，这应该是大城市规模的第一个模型。实验结果表明，TCD-AIM会发现瓶颈，将来拥塞密度会增加多达130％。从影响的角度来看，这应该是大城市规模的第一个模型。实验结果表明，TCD-AIM会发现瓶颈，将来拥塞密度会增加多达130％。从影响的角度来看，这应该是大城市规模的第一个模型。实验结果表明，TCD-AIM会发现瓶颈，将来拥塞密度会增加多达130％。