利用信息熵方法了解城市路网路径流量分布的可预测性

Bao Guo , Zhiren Huang , Zhihao Zheng , Fan Zhang , Pu Wang
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引用次数: 0

摘要

预测城市道路网络中起点-终点(OD)对之间的路径流量分布对于制定高效的交通控制和管理策略至关重要。在此,我们利用旧金山和深圳的大规模出租车 GPS 轨迹数据来研究城市道路网络中路径流分布的可预测性。我们开发了一种将时变路径流量分布投影到高维空间的方法。在高维空间中,信息熵被用来衡量路径流分布的可预测性。我们发现,OD 对之间的路径流分布一般具有较高的可预测性。此外,我们还分析了影响路径流分布可预测性的因素。最后,我们提出了一个包含高阶克和低阶克的 n-gram 模型来预测路径流的分布。预测精度相对较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.

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