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首页> 外文期刊>Concurrency and computation: practice and experience >Dynamic traffic bottlenecks identification based on congestion diffusionmodel by influencemaximization inmetro-city scales
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Dynamic traffic bottlenecks identification based on congestion diffusionmodel by influencemaximization inmetro-city scales

机译：基于充血扩散模型的动态流量瓶颈识别型蔓延化内部鳞片

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摘要

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.
机译：交通瓶颈随着交通需求的方差而动态变化。识别交通瓶颈在交通规划中发挥着重要作用，并提供决策。然而，由于交通道路网络和许多其他因素的复杂性，难以识别流量瓶颈。在本文中，我们提出了一种基于影响的方法，找到了动态改变的流量瓶颈，其中由瓶颈引起的影响是最大的。我们首先建立交通拥堵扩散（TCD）模型，以捕获传播交通道路网络的流量影响（TFI）。基于TCD的瓶颈识别问题被建模为影响最大化问题，即选择最有影响力的节点，使得交通状况的恶化是最大的。随着TFI潜水性的证据，通过交通网络传播，可提供可怕的接近最佳算法来解决NP-Coll问题。随着TFI传播独特性质的探索，提出了一种近似影响TCD（TCD-AIM）的最大化方法。据我们所知，这应该是来自影响力的地铁城规模的第一款。实验结果表明，TCD-AIM在未来发现瓶颈达130％的拥堵密度增加。

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