分享与训练:一种新的方法来改进共享单车系统的预测任务
Ahmed Ali1,2, Ahmad Salah3,4, Mahmoud Bekhit5,6
1Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Mathematical biosciences and engineering : MBE
|August 23, 2024
概括
这项研究引入了一种新的"划分和训练"方法,用于自行车共享系统 (BSS). 通过对用户数据进行细分,这种方法可以提高旅行时间和距离的预测模型的准确性,优于传统的培训方法.
科学领域:
- 智慧城市技术 智慧城市技术
- 数据科学和机器学习
- 运输系统分析 运输系统分析
背景情况:
- 自行车共享系统 (BSS) 是智能城市不可或缺的一部分,产生大量数据集.
- 在BSS中有效的决策依赖于及时的预测.
- 现有的预测模型通常在整个数据集上进行训练,忽略了不同的用户模式.
研究的目的:
- 提出和验证一种新的方法,用于培训基于BSS数据的预测模型.
- 通过考虑特定用户模式来解决传统培训方法的局限性.
- 在BSS中提高行程持续时间和距离预测的准确性.
主要方法:
- 引入了"分割和训练"方法,根据用户属性分割BSS数据集.
- 使用各种机器学习和深度学习模型验证了拟议的方法.
- 在完整数据集上训练的模型性能与分割的子数据集进行了比较.
主要成果:
- 与传统培训相比",划分和训练"方法显示出更高的性能.
- 对于行程持续时间和距离预测的根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 观察到显著的改善.
- 性能最好的模型,随机森林,在分割的子数据集中实现了平均85%的准确性.
结论:
- "划分和训练"方法为在BSS中构建预测模型提供了更有效的策略.
- 基于用户属性的个性化模型训练可以提高预测的准确性.
- 这种方法有助于在城市移动方面做出更有效,更明智的决策.
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