一个统一的时空推理网络用于汽车共享系列预测
Nihad Brahimi1, Huaping Zhang1, Syed Danial Asghar Zaidi1
1School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
准确的汽车共享需求预测对于高效运营至关重要. 新的统一时空推理预测网络 (USTIN) 有效地模拟复杂的时空因素,优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 运输系统 运输系统
背景情况:
- 汽车共享系统需要精确的需求预测,以实现最佳的资源配置和调度.
- 准确的汽车需求预测受到复杂的时空相互依赖的阻碍.
研究的目的:
- 引入USTIN (统一空间时间推理预测网络),这是一个用于增强汽车共享需求预测的新型神经网络.
- 开发一种能够捕捉汽车共享需求中复杂的时间,空间和空间-时间关系的模型.
主要方法:
- 开发了USTIN,一个具有独特的时间,空间和空间-时间特征单元的神经网络.
- 时间单位在每小时,每天,每周和每月的尺度上处理历史数据.
- 空间单元整合了感兴趣的数据点;时空单元整合了天气数据.
主要成果:
- 乌斯有效地从现实世界的汽车共享数据中学习了复杂的时空需求模式.
- 拟议的USTIN模型显著优于现有的最先进的预测方法.
- 负二项回归确定了影响汽车使用模式的关键因素.
结论:
- 乌斯为准确的汽车共享需求预测提供了强大的解决方案.
- 该模型集成多种数据源的能力提高了其预测能力.
- 了解影响因素可以进一步优化汽车共享系统管理.
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