开发一个过境沙漠交互式仪表板:监督建模用于预测过境沙漠
Seung Jun Choi1, Junfeng Jiao1
1Urban Information Lab, The School of Architecture, The University of Texas at Austin, Austin, TX, United States of America.
PloS one
|July 24, 2024
概括
这项研究使用机器学习来识别过境沙漠,发现密度和设计是关键因素. 解决方案包括减少密度和增加绿色空间,重点关注性别特定的运输需求.
科学领域:
- 城市规划 城市规划
- 运输科学 运输科学
- 数据科学数据科学数据科学
背景情况:
- 过境沙漠,公共交通不足的地区,不成比例地影响某些人口.
- 了解导致过境沙漠的因素对于公平的城市发展至关重要.
研究的目的:
- 使用机器学习框架识别导致过境沙漠的因素.
- 提出可行的解决方案,将过境沙漠转化为过境绿洲.
- 分析运输需求,考虑到高峰时段的性别差异.
主要方法:
- 采用了多类监督机器学习框架,评估了支持矢量机,决策树,随机森林和K-最近邻近算法.
- 选择了随机森林模型,并通过多元反事实解释和夏普利添加式解释进行了深入分析.
- 排列特征的重要性,揭示密度,设计,运输距离,建筑环境多样性和社会人口统计作为重要因素.
主要成果:
- 导致过境沙漠的关键因素包括人口密度,交通便利性和城市设计.
- 不同的反事实解释表明,减少人口密度和增加绿色空间可以缓解过境沙漠.
- 沙普利的附加解释揭示了各种过境沙漠特征的差异性影响,突出了性别特定的过境需求,特别是女性.
结论:
- 过境沙漠的识别和解决方案受到数据聚合和人口分离的影响,特别是按性别.
- 解决过境沙漠问题需要优先考虑弱势群体,改善过境设计和可访问性.
- 机器学习模型,交互式仪表板和参与式规划可以推进公平的交通解决方案.
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