一个基于适应期望-最大化注意力的时空概率图形模型,用于考虑不完整的观测来重建个体轨迹
Xuan Sun1,2, Jianyuan Guo1, Yong Qin2
1School of Traffic and Transportation, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, China.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
本研究重建了城市铁路运输轨迹,使用了新的时空概率图形模型,以适应期望最大化注意力 (STPGM-AEMA). 该方法准确地恢复缺失的轨迹数据,改进运营策略和个性化建议.
科学领域:
- 城市交通系统城市交通系统
- 数据科学和数据分析
- 可能性的建模.
背景情况:
- 准确的个体轨迹数据对于城市铁路运输运营至关重要.
- 现有的方法很难从有限的自动收费 (AFC) 和自动车辆位置 (AVL) 数据中推断出缺失的轨迹信息.
研究的目的:
- 提出一种用于重建城市铁路运输中的单个轨迹的新方法.
- 为了提高从不完整的轨迹数据中推断缺少的时空信息的准确性.
主要方法:
- 开发了一个基于适应期望最大化注意力 (STPGM-AEMA) 的空间时间概率图形模型.
- 包含数据挖掘和组合计数,以确定潜在的列车和出站时间替代方案.
- 利用全球和本地潜在变量推断未知的轨迹事件.
- 采用了注意力机制增强的预期最大化算法,用于最大概率估计.
主要成果:
- 该STPGM-AEMA方法在恢复缺失的轨迹信息方面取得了超过95%的准确性.
- 与PTAM-MLE和MPTAM-EM等传统方法相比,其准确度至少提高了15%.
- 使用来源-目的地对数据集和真实个体轨迹跟踪数据验证的有效性.
结论:
- 该STPGM-AEMA方法显著提高了城市铁路运输轨道的重建.
- 这一进步为操作战略调整,个性化建议和紧急决策提供了更好的能力.
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