计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (9): 65-78.DOI: 10.3778/j.issn.1002-8331.2308-0127
杨晨曦,庄旭菲,陈俊楠,李衡
出版日期:
2024-05-01
发布日期:
2024-04-29
YANG Chenxi, ZHUANG Xufei, CHEN Junnan, LI Heng
Online:
2024-05-01
Published:
2024-04-29
摘要: 公交行驶轨迹预测是对公交车到达线路上的重要轨迹点,如站点和道路交叉口等,进行到达时间预测。准确预测公交车到达站点和道路交叉口的时间,可以提高城市公交系统的运行效率和服务质量,对于城市公共交通规划和公交调度至关重要。从公交行驶轨迹预测方法的发展现状入手,分析了影响公交运行的相关因素,归纳并探讨了不同类型的相关数据集以及数据预处理方法。依照其发展脉络将公交行驶轨迹预测方法分为基于历史数据的模型、以时间序列模型为代表的参数模型以及包括机器学习和深度学习方法的非参数模型三大类,并总结分析了不同方法的优势和局限性。由于基于深度学习的相关模型在时间序列预测任务中表现出了优越性能,因此越来越多的学者开始采用基于深度学习的模型来解决公交行驶轨迹预测问题,同时考虑将城市道路所展现的空间特征与时间特征相结合以进一步提高预测精度。最后,阐述了公交行驶轨迹预测研究领域中面临的挑战,并对该领域未来的发展进行总结分析与趋势展望。
杨晨曦, 庄旭菲, 陈俊楠, 李衡. 基于深度学习的公交行驶轨迹预测研究综述[J]. 计算机工程与应用, 2024, 60(9): 65-78.
YANG Chenxi, ZHUANG Xufei, CHEN Junnan, LI Heng. Review of Research on Bus Travel Trajectory Prediction Based on Deep Learning[J]. Computer Engineering and Applications, 2024, 60(9): 65-78.
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