道路网络和轨迹的多目标表示学习,具有时空融合和对比信号
Ashraful Islam Shanto Sikder1, Naushin Nower1
1Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh.
PloS one
|September 18, 2025
概括
这项研究引入了学习道路网络和车辆轨迹表示的新框架,改进了智能交通系统. 多目标方法提高了交通预测和行程时间估计等任务的性能.
科学领域:
- 智能运输系统 智能运输系统
- 机器学习 机器学习
- 时间空间数据分析数据分析
背景情况:
- 对道路网络和车辆轨迹的有效建模对于智能交通系统至关重要.
- 挑战包括异质的时空数据和对表示学习的有限监督.
- 现有的方法很难在各种任务中进行概括.
研究的目的:
- 提出一个统一的多目标预培训框架,用于学习道路网络和轨迹的表示.
- 解决当前处理复杂的时空空间城市流动数据的方法的局限性.
- 提高下游智能运输任务的概括能力.
主要方法:
- 引入了道路网络和轨迹 (MRRT) 框架的多目标表示学习.
- 结合面具轨迹建模 (MTM) 与跨轨迹,道路段和空间背景的对比学习.
- 集成图表注意网络 (GAT),空间CNN和具有时间/位置编码的变压器,增强了轨迹特定数据增强和自适应负采样.
主要成果:
- 该MRRT框架有效地捕捉了城市流动中的结构和上下文依赖.
- 在下游任务中表现出卓越的性能,包括旅行时间估计,速度推断和相似性搜索.
- 在广泛的实验中超越了先前的基线和废弃的变体,验证了多目标设计.
结论:
- 拟议的MRRT框架为智能运输系统中的学习表示提供了强大而有效的解决方案.
- 多目标预培训显著提高了各种道路网络和基于轨迹的任务的概括性.
- 该方法为未来在城市流动性分析和预测方面的进步提供了坚实的基础.
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