Street Network Generation with Adjustable Complexity Using k-Means Clustering
Street Network Generation with Adjustable Complexity Using k-Means Clustering
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DOI：
10.1109/southeastcon42311.2019.9020392
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Quentin Goss;M. Akbaş;L. Jaimes;R. Sanchez-Arias
中科院分区：
文献类型：
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作者：
Quentin Goss;M. Akbaş;L. Jaimes;R. Sanchez-Arias
交通运输系统是世界上大部分人口日常生活的重要组成部分。因此，创新的应用程序被设计为在技术进步允许的情况下改善驾驶员和行人在该系统中的体验。这些应用程序分析街道网络，提出最快路线或最佳拼车路线等建议。这些分析经常使用图论，其中每个交叉点由顶点表示，每个路段由边表示。然而，应用程序也需要这些顶点的分组，以便他们可以运行优化方法在更大的scale.In本文中，我们提出了一种方法来创建街道网络的可调复杂性。通过使用k-Means聚类，我们的机制允许增加或减少街道网络的梯度。实现结果表明，该方法的效率和灵活性，提供可调的复杂度的街道网络图。
The transportation system is an important part of the daily lives of a major portion of the world’s population. Therefore, innovative applications are designed to improve the experience of drivers and pedestrians in this system as technological advances allow. These applications analyze street networks to come up with suggestions such as fastest route or optimal ride-sharing route. Graph theory has been used frequently for these analyses, where each junction is represented by a vertex and each road segment is represented by an edge. However, applications also require grouping of these vertices so that they can run optimization methods in a larger scale.In this paper, we propose a method for creating street networks with adjustable complexity. By using k-Means clustering, our mechanism allows for the increase or decrease of the gradient of a street network. The implementation results demonstrate the proposed method’s efficiency and flexibility for providing street network graphs with adjustable complexity.