Luo Dan, Zhao Fang, Zhou Hao, Wang Chenxing, Xiong Hao
The School of Information Science and Technology, School of Artificial Intelligence, Beijing Forestry University, Beijing, China.
The School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.
PLoS One. 2025 Jun 6;20(6):e0325471. doi: 10.1371/journal.pone.0325471. eCollection 2025.
Individual Trip Destination Prediction aims to accurately forecast an individual's future travel destinations by analyzing their historical trajectory data, holding significant application value in intelligent navigation, personalized recommendations, and urban traffic management. However, challenges such as data sparsity, low quality, and complex spatiotemporal volatility pose substantial difficulties for prediction tasks. Existing studies exhibit notable limitations in insufficient integration of sparsity handling and prediction tasks, constrained modeling capability for local volatility, and inadequate exploration of fine-grained spatial dependencies, struggling to balance global patterns and local features in trajectory data. To address these issues, this paper proposes an individual trip destination prediction method that integrates multi-task learning, a multi-trajectory subsequence alignment attention mechanism, and a spatially consistent constrained cross-entropy loss function. Leveraging a multi-task learning framework(MTSA-SC), our approach collaboratively addresses trajectory recovery and prediction tasks, enhancing prediction accuracy while improving robustness to missing data. The multi-trajectory subsequence alignment attention mechanism incorporates sliding windows and convolutional operations to dynamically capture local volatility and diverse patterns in trajectories. The spatially consistent constrained loss function strengthens spatial feature learning through differential error penalty adjustments. Experimental results on public datasets from Shenzhen and Xiamen demonstrate recall rates of 0.722 and 0.6 under complete and sparse trajectory scenarios, respectively, outperforming state-of-the-art baselines by an average of 15.64%. This research provides robust technical support for intelligent travel recommendations and traffic management.
个体出行目的地预测旨在通过分析个体的历史轨迹数据来准确预测其未来的出行目的地，在智能导航、个性化推荐和城市交通管理方面具有重要的应用价值。然而，数据稀疏、质量低以及复杂的时空波动性等挑战给预测任务带来了巨大困难。现有研究在稀疏性处理与预测任务的整合不足、对局部波动性的建模能力受限以及对细粒度空间依赖性的探索不足等方面存在显著局限性，难以在轨迹数据中平衡全局模式和局部特征。为了解决这些问题，本文提出了一种个体出行目的地预测方法，该方法集成了多任务学习、多轨迹子序列对齐注意力机制和空间一致约束交叉熵损失函数。利用多任务学习框架（MTSA-SC），我们的方法协同处理轨迹恢复和预测任务，提高预测准确性，同时增强对缺失数据的鲁棒性。多轨迹子序列对齐注意力机制结合滑动窗口和卷积操作，动态捕捉轨迹中的局部波动性和多样模式。空间一致约束损失函数通过差分误差惩罚调整加强空间特征学习。在深圳和厦门的公共数据集上的实验结果表明，在完整轨迹和稀疏轨迹场景下，召回率分别为0.722和0.6，平均比现有最先进的基线方法高出15.64%。本研究为智能出行推荐和交通管理提供了有力的技术支持。
