用混合RidgeGAN模型预测印度中小城市的城市模式的交通指数
Rahisha Thottolil1, Uttam Kumar1, Tanujit Chakraborty2,3,4
1Spatial Computing Laboratory, Center for Data Sciences, International Institute of Information Technology, Bangalore, 560100, India.
Scientific reports
|December 9, 2023
概括
这项研究介绍了RidgeGAN,这是一种预测印度城市道路网络密度的混合模型. 它通过改善交通基础设施,帮助城市规划者创造可持续,宜居的城市区域.
科学领域:
- 城市规划和交通地理 城市规划和交通地理
- 地理信息学和空间分析
- 城市研究中的人工智能
背景情况:
- 像印度这样的发展中国家的快速城市化在包括交通在内的公民设施方面带来了挑战.
- 城市和地区道路系统复杂,需要使用交通指数进行分析,例如网络密度.
- 印度的中小型城市在诸如中小型城市综合发展 (IDSMT) 等项目下面临着独特的发展压力.
研究的目的:
- 预测印度中小型城市的道路网络密度.
- 使用无监督的CityGAN模型生成现实的城市模式.
- 通过使用Kernel Ridge回归 (KRR) 调查人类定居点指数和网络密度之间的关系.
主要方法:
- 一个混合框架,将Kernel Ridge回归 (KRR) 和CityGAN (称为RidgeGAN) 结合起来.
- 使用人类定居点的空间指标来预测网络密度.
- 分析了503个真正的印度城市,以确定城市定居点和道路密度之间的关系.
主要成果:
- RidgeGAN模型有效地预测了各种人类定居点类型的网络密度,特别是在印度中小型城市.
- 非线性KRR模型可以为生成的城市模式推导网络密度.
- 这项研究证明了RidgeGAN在评估城市扩张的可持续性方面的实用性.
结论:
- RidgeGAN为城市规划人员提供了一种有价值的工具,可以预测网络密度并了解城市扩张.
- 该模型通过向交通基础设施发展提供信息,有助于创造更宜居和可持续的城市环境.
- 这项研究通过预测未来的城市模式,支持对发展中城市进行更好的城市规划.
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