广东工业大学学报 ›› 2023, Vol. 40 ›› Issue (04): 1-8.doi: 10.12052/gdutxb.220157
• 计算机科学与技术 • 下一篇
温雯1, 刘莹1, 蔡瑞初1, 郝志峰1,2
Wen Wen1, Liu Ying1, Cai Rui-chu1, Hao Zhi-feng1,2
摘要: 交通流预测是智能交通系统中的一类重要问题。尽管当前交通流预测方法取得了较好的进展,但还面临2个关键挑战:(1) 交通流的变化模式不仅依赖于时间维度上的历史信息,还依赖于空间维度上相邻区域的信息,如何兼顾两个维度上的变化模式;(2) 时间本身具有小时、天及周等多粒度特性,如何实现多粒度下时序模式的捕捉。本文针对交通流预测的上述挑战,设计了一个多粒度时空深度回归模型(Spatial-temporal Deep Regression Model for Multi-granularity, MGSTDR),其基本思想是在多粒度时空交通流信息的基础上,对典型的差分整合移动平均自回归模型(Autoregressive Integrated Moving Average model, ARIMA)进行深度拓展,该模型在有效利用自身历史信息的同时,能兼顾相邻区域的信息,从而能够实现多粒度的时序交通流量预测。多个数据集上的实验结果表明,该模型在多粒度预测任务上优于现有的多个基准模型,尤其在小时这一粒度的预测结果上有5.66%的提升。
中图分类号:
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