Pei Xiaohui, Yang Xianjun, Wang Tao, Ding Zenghui, Xu Yang, Jia Lin, Sun Yining
University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei, 230026, Anhui, China.
Hefei Institutes of Physical Science, Chinese Academy of Sciences, No. 350, Shushanhu Road, Hefei, 230031, Anhui, China.
Heliyon. 2024 Aug 5;10(15):e35572. doi: 10.1016/j.heliyon.2024.e35572. eCollection 2024 Aug 15.
Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.
识别出行方式对于现代城市交通规划与管理至关重要。数据收集方面的最新进展，尤其是那些涉及全球定位系统（GPS）技术的进展，为快速准确地推断用户出行方式提供了广阔的机会。本研究提出了一种从GPS轨迹数据推断出行方式的创新方法。该方法利用多尺度卷积技术来捕捉和分析数据的时空信息，从而揭示用户移动和行为模式中固有的潜在时空关系。此外，模型中集成了注意力机制以实现自主学习。该机制增强了模型识别和强调不同时间段和空间位置关键信息的能力，从而提高了出行方式推断的准确性。在开源GPS轨迹数据集GeoLife上的评估表明，所提出的方法准确率达到了83.3%。这一结果突出了该方法的有效性，表明该模型通过多尺度卷积技术和注意力机制的整合能够更准确地理解和预测用户出行方式。
