在城市聚合区的人类流动性的多个规模统一模型
Yong Chen1, Haoge Xu1, Xiqun Michael Chen1,2
1Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China.
Patterns (New York, N.Y.)
|November 30, 2023
概括
这项研究引入了一种新的深度学习模型,用于预测大型城市地区在多个规模上的人类流动性. 该框架通过了解复杂的旅行行为,使更好的城市规划成为可能.
科学领域:
- 城市规划和计算社会科学.
- 地理信息科学和运输建模.
背景情况:
- 协调的城市发展依赖于了解人类流动模式.
- 现有的模式努力统一城市聚集区内的多层次人类流动性.
研究的目的:
- 开发一个统一的模型,用于预测城市聚合区的多个规模的人类流动.
- 解决现有移动模式的分析和计算挑战.
主要方法:
- 将人类的旅行选择分解成一个级联的多类分类问题.
- 使用级联深度神经网络进行多层次预测.
- 通过图形生成对抗网络将个人记忆和人口吸引力特征纳入.
主要成果:
- 成功预测了成千上万个地区的大型城市聚集区的人类流动性.
- 同时建模多个规模的个人和人口流动模式.
- 展示了复制人类流动的普遍规律的框架.
结论:
- 开发的模型提供了一种统一的方法,用于在不同的空间尺度上对人类移动性的建模.
- 提供城市规划和城市聚合区发展的重要决策支持.
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