一个监督的机器学习模型,用于在智能卡数据中赋值错过的登机站
Nadav Shalit1, Michael Fire1, Eran Ben-Elia2
1Data4Good Lab, Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Public transport (Heidelberg, Germany)
|April 16, 2024
概括
这项研究引入了一种机器学习方法,使用智能卡数据准确地计算出错失的公共交通登机站. 该方法通过提高数据完整性来增强旅行行为分析和运输规划.
科学领域:
- 运输科学 运输科学
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 公共交通对于城市流动性至关重要,产生大量的智能卡数据用于旅行行为分析.
- 数据完整性问题,例如缺少登机站信息,妨碍了准确的分析.
- 现有的方法在不完整的公共交通数据集中扎.
研究的目的:
- 开发一种监督的机器学习方法,用于归因缺失的公共交通登机站.
- 为顺序分类任务引入一个新的评估指标,帕雷托准确度.
- 评估该方法的稳定性,通用性和性能与现有的归算技术相比.
主要方法:
- 利用基于顺序分类的监督机器学习方法.
- 综合通用运输料规范 (GTFS) 时间表,智能卡和地理空间数据.
- 开发并应用了一种新的度量,帕雷托精度,用于评估顺序归算算法.
主要成果:
- 拟议的方法准确地归因错过的登机站,优于传统的归因技术.
- 该方法证明了对不规则的旅行模式的稳定性,并且不需要额外的数据挖掘.
- 转移学习验证证实了该模型在不同城市环境中的通用性.
结论:
- 开发的机器学习模型有效地解决了公共交通系统中缺少的数据的问题.
- 帕雷托准确度指标为顺序分类问题提供了可靠的评估.
- 这项研究对通过提高数据质量来加强交通规划和旅行行为研究具有重大意义.
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