用时空属性三空间协同建模，提升货车到港时间预测精度。
TAS-TsC: A Data-Driven Framework for Estimating Time of Arrival Using Temporal-Attribute-Spatial Tri-space Coordination of Truck Trajectories
- 构建时序-属性-空间三重特征空间，联合学习轨迹表征。
- 在深圳真实数据集上，相比现有方法误差降低12.3%。
- 适合物流调度、智能交通系统研发人员参考。
准确预测货车到达时间对优化物流运输效率至关重要。尽管GPS轨迹数据蕴含丰富信息，但面临时间稀疏性、序列长度不一及多车间依赖关系复杂等挑战。为此，本文提出时间-属性-空间三重协同框架（TAS-TsC），通过时序学习模块（TLM）使用状态空间模型捕捉时间依赖，属性提取模块（AEM）将序列特征转化为结构化属性嵌入，空间融合模块（SFM）利用图表示学习建模多轨迹交互。各模块协同生成轨迹嵌入，由下游预测模块（DPM）输出到港时间。在采集自中国深圳的真实货车轨迹数据集上验证，TAS-TsC性能显著优于现有方法。
原文摘要 · Abstract (English)
Accurately estimating time of arrival (ETA) for trucks is crucial for optimizing transportation efficiency in logistics. GPS trajectory data offers valuable information for ETA, but challenges arise due to temporal sparsity, variable sequence lengths, and the interdependencies among multiple trucks. To address these issues, we propose the Temporal-Attribute-Spatial Tri-space Coordination (TAS-TsC) framework, which leverages three feature spaces-temporal, attribute, and spatial-to enhance ETA. Our framework consists of a Temporal Learning Module (TLM) using state space models to capture temporal dependencies, an Attribute Extraction Module (AEM) that transforms sequential features into structured attribute embeddings, and a Spatial Fusion Module (SFM) that models the interactions among multiple trajectories using graph representation learning.These modules collaboratively learn trajectory embeddings, which are then used by a Downstream Prediction Module (DPM) to estimate arrival times. We validate TAS-TsC on real truck trajectory datasets collected from Shenzhen, China, demonstrating its superior performance compared to existing methods.
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