This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

本研究解决了基于位置的服务中的一个关键问题：出行路线推荐。它利用历史轨迹数据来预测道路网络上从起点到目的地的实际路线，给定特定的出发时间。然而，在复杂的轨迹数据中捕获潜在模式以进行准确的路线规划是一项重大挑战。现有的路线推荐方法普遍面临两大问题：一是多源数据整合不足，未能充分考虑影响路线选择的潜在因素;其次，捕获路网特征的能力有限，这限制了节点特征的有效应用并对推荐准确性产生负面影响。为了解决这些问题，本研究引入了一种基于深度学习技术的路线推荐轨迹学习模型 （TLMR）。TLMR 通过集成多源数据，增强了对复杂环境中用户路由选择行为的理解。此外，通过整合路网特征，TLMR 可以更有效地捕获和利用路网的结构和动态信息。具体来说，TLMR 首先采用位置感知图神经网络从道路网络中学习交叉路口的特征，并结合天气和交通状况等上下文特征。然后，它通过神经网络整合这些信息来预测下一个交叉路口。最后，应用波束搜索算法生成和推荐多条候选路线。在四个大型真实数据集上的广泛实验表明，TLMR 在四个关键性能指标上优于现有方法。这些结果证明了 TLMR 在路线推荐中的有效性和优越性。