On a Clustering-Based Approach for Traffic Sub-area Division
On a Clustering-Based Approach for Traffic Sub-area Division
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DOI：
10.1007/978-3-030-22999-3_45
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Jiahui Zhu;Xinzheng Niu;C. Wu
中科院分区：
文献类型：
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作者：
Jiahui Zhu;Xinzheng Niu;C. Wu
交通分区划分是交通管理与控制中的一个重要问题。本文提出了一种基于聚类的方法来解决这一问题，该方法同时考虑了车辆轨迹的时空信息。考虑到时间和空间上的不同数量级，采用az-score方案实现均匀性，并基于新的密度定义和相似度度量设计了改进的密度峰聚类方法提取热点区域。我们设计了一种基于分布的分区方法，利用mean算法将热点区域划分为一组流量子区域。在性能评价方面，我们基于该指标和文献中经典的davis - bouldin指数建立了交通分区划分标准。实验结果表明，该方法比现有方法提高了交通分区划分的质量。
Traffic sub-area division is an important problem in traffic management and control. This paper proposes a clustering-based approach to this problem that takes into account both temporal and spatial information of vehicle trajectories. Considering different orders of magnitude in time and space, we employ az-score scheme for uniformity and design an improved density peak clustering method based on a new density definition and similarity measure to extract hot regions. We design a distribution-based partitioning method that employsk-means algorithm to split hot regions into a set of traffic sub-areas. For performance evaluation, we develop a traffic sub-area division criterium based on theindicator and the classical Davies-Bouldin index in the literature. Experimental results illustrate that the proposed approach improves traffic sub-area division quality over existing methods.