地铁站的高峰时间偏差基于最小方形支向量机的偏差
Lijie Yu1, Mengying Cui1, Shian Dai2
1College of Transportation Engineering, Chang'an University, Xi'an, Shaanxi, China.
PloS one
|September 13, 2023
概括
地铁乘客预测必须考虑到峰值偏差. 一个新的土地使用指数通过分析车站峰值与城市峰值需求来改善预测,防止低估和运营问题.
科学领域:
- 运输工程 运输工程
- 城市规划 城市规划
- 数据科学数据科学数据科学
背景情况:
- 站级乘客对于地铁设计至关重要,但车站和城市之间的高峰时间往往不同.
- 目前的预测方法,以城市峰值为参考,导致低估和潜在的干扰不同峰值需求的车站.
研究的目的:
- 调查影响地铁乘客峰值偏差系数 (PDC) 的因素.
- 开发一种更准确的车站级乘客预测方法,可以考虑不同的高峰时间.
主要方法:
- 引入了峰值偏差系数 (PDC) 作为车站峰值乘客数与城市峰值乘客数的比率.
- 采用最小正方形支向量机 (LSSVM) 模型来分析影响PDC的乘客决定因素.
- 提出并使用了一个新的土地使用功能互补指数来描述与网络相对的当地通勤土地使用.
主要成果:
- LSSVM模型有效地揭示了土地使用和地铁乘客的时间分布之间的非线性关系.
- 拟议的土地使用函数互补性指数显示,与简单的通勤土地使用比率相比,峰值偏差的解释和预测能力更强.
- 该研究成功提供了一种细粒度,站级乘客预测的方法,解决了峰值偏差.
结论:
- 为了准确的规划,地铁乘客预测需要纳入峰值偏差现象.
- 土地使用函数互补性指数是理解和预测峰值偏差的一个有价值的工具.
- LSSVM是一种有效的技术,用于分析地铁乘客的复杂,非线性因素.
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