一种基于多信息融合的城市道路交通流量预测方法
Scientific reports
|February 15, 2025
概括
多信息融合预测网络 (MIFPN) 通过将长期和短期的历史数据与天气等外部因素相结合,改善了流量预测. 这种新的方法提高了未来交通流量预测的准确性.
科学领域:
- 运输科学 运输科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的流量预测对于智能交通系统至关重要.
- 现有的方法往往侧重于短期预测,并努力纳入各种外部因素.
- 空间时间图神经网络 (STGNN) 捕捉空间和时间的依赖性,但忽视长期趋势和周期性模式.
研究的目的:
- 提出一个新的多信息融合预测网络 (MIFPN) 进行增强的流量预测.
- 有效地整合历史交通数据,外部因素以及长期和短期时间特征.
- 提高交通流量预测的准确性和可靠性,特别是在较长的时间范围内.
主要方法:
- 使用后续转换器从包含外部信息的扩展历史序列中学习时间关系.
- 采用叠加的一维膨胀卷积层用于长期趋势提取.
- 实现了一个动态图的卷积层用于周期性特征提取和一个短期趋势提取器用于即时时间动态.
- 化提取了长期趋势,周期性特征和短期特征,用于最终预测.
主要成果:
- MIFPN模型在流量预测准确度方面取得了显著的改进.
- 在长期预测 (长达60分钟) 中,比基线模型平均改善了11.2%.
- 成功整合了各种数据源,包括历史交通数据和外部因素,如天气和POI分布.
结论:
- 拟议的MIFPN有效地捕捉了复杂的时间依赖性,并将多源信息合并为优越的流量预测.
- 该模型能够提取长期趋势和短期特征,以及周期性模式,从而提高预测准确度.
- 对于需要可靠的长期交通流量预测的智能交通系统,MIFPN提供了一个有希望的进步.
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