F-Deepwalk:交通网络的社区检测模型
Jiaao Guo1, Qinghuai Liang1, Jiaqi Zhao1
1School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China.
Entropy (Basel, Switzerland)
|August 29, 2024
概括
本研究引入了一种改进的F-Deepwalk模型,用于在交通网络中检测城市社区. 改进的模型通过考虑通用旅行成本,改进网络设计,准确地识别社区.
科学领域:
- 城市规划和交通网络分析.
- 计算社会科学和网络科学.
背景情况:
- 交通网络设计通常依赖于将大都市区划分为社区.
- 精确的社区划分对于优化运输路线,车站位置和基于规模,人口密度和旅行行为的社区间旅行联系至关重要.
研究的目的:
- 开发一种改进的方法,用于在交通网络中检测城市空间社区.
- 增强经典的Deepwalk模型,以实现更精确的社区识别和交通网络规划.
主要方法:
- 修改了经典的Deepwalk模型,提出了一个随机步行 (RW) 算法,具有通用的旅行成本和改进的逻辑模型.
- 采用K-means算法来检测城市空间社区,从而产生F-Deepwalk模型.
- 使用基本道路网络和石家庄城市铁路运输网络验证了该模型.
主要成果:
- 与基本的Deepwalk模型相比,F-Deepwalk模型结合了通用旅行成本,显示了更高的配置系数.
- 随着随机步行长度的减少,模型的性能得到了改善.
- 该模型的准确性通过使用现实世界城市铁路运输网络进一步得到证实.
结论:
- F-Deepwalk模型为交通网络设计提供了更准确的城市社区检测方法.
- 考虑到通用旅行成本,这大大提高了社区检测算法的有效性.
- 这种方法为优化城市交通系统提供了有价值的工具,以更好地满足居民的旅行需求.
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