MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning
MTrajRec: Map-Constrained Trajectory Recovery via Seq2Seq Multi-task Learning
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DOI：
10.1145/3447548.3467238
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Hu Ren;Sijie Ruan;Yanhua Li;Jie Bao;Chuishi Meng;Ruiyuan Li;Yu Zheng
中科院分区：
文献类型：
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作者：
Hu Ren;Sijie Ruan;Yanhua Li;Jie Bao;Chuishi Meng;Ruiyuan Li;Yu Zheng
随着GPS模块的越来越多的采用，基于轨迹数据分析（例如车辆导航，旅行时间估算和驾驶员行为分析）的城市应用程序广泛。城市应用的有效性在很大程度上取决于与地图完全匹配的轨迹的高采样率。但是，由于某些通信损失和能量限制，在现实世界实践中，在低采样率下收集了大量轨迹。为了增强轨迹数据并更有效地支持城市应用程序，提出了许多轨迹恢复方法来推断自由空间中的轨迹。此外，在应用程序中，仍需要将恢复的轨迹映射到道路网络。但是，两阶段的管道首先不准确且效率低下。在本文中，我们提出了一个受映射轨迹恢复框架Mtrajrec，以恢复轨迹中的细粒点，并以端到端方式在道路网络上匹配它们。 Mtrajrec实现了多任务序列与序列学习体系结构，以同时预测路段和移动比率。提出了约束面罩，注意机制和属性模块，以克服粗网格表示的限制并改善性能。基于大规模现实世界轨迹数据的广泛实验证实了我们方法的有效性和效率。
With the increasing adoption of GPS modules, there are a wide range of urban applications based on trajectory data analysis, such as vehicle navigation, travel time estimation, and driver behavior analysis. The effectiveness of urban applications relies greatly on the high sampling rates of trajectories precisely matched to the map. However, a large number of trajectories are collected under a low sampling rate in real-world practice, due to certain communication loss and energy constraints. To enhance the trajectory data and support the urban applications more effectively, many trajectory recovery methods are proposed to infer the trajectories in free space. In addition, the recovered trajectory still needs to be mapped to the road network, before it can be used in the applications. However, the two-stage pipeline, which first infers high-sampling-rate trajectories and then performs the map matching, is inaccurate and inefficient. In this paper, we propose a Map-constrained Trajectory Recovery framework, MTrajRec, to recover the fine-grained points in trajectories and map match them on the road network in an end-to-end manner. MTrajRec implements a multi-task sequence-to-sequence learning architecture to predict road segment and moving ratio simultaneously. Constraint mask, attention mechanism, and attribute module are proposed to overcome the limits of coarse grid representation and improve the performance. Extensive experiments based on large-scale real-world trajectory data confirm the effectiveness and efficiency of our approach.