Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction

Wen Wen; Liu Ying; Cai Rui-chu; Hao Zhi-feng

doi:10.12052/gdutxb.220157

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Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction

更新时间：2026-05-27

- Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction
- Vol. 40, Issue 4, Pages: 1-8(2023)
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 汕头大学工学院,广东,汕头,515000
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220157
  CLC： TP389.1
- Online First：25 July 2023，
  
  Published：2023
- 稿件说明：
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温雯, 刘莹, 蔡瑞初, 郝志峰. 面向多粒度交通流预测的时空深度回归模型[J]. 广东工业大学学报, 2023, 40(4): 1-8.

Wen Wen, Liu Ying, Cai Rui-chu, et al. Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction[J]. 2023, 40(4): 1-8.
温雯, 刘莹, 蔡瑞初, 郝志峰. 面向多粒度交通流预测的时空深度回归模型[J]. 广东工业大学学报, 2023, 40(4): 1-8. DOI： 10.12052/gdutxb.220157.

Wen Wen, Liu Ying, Cai Rui-chu, et al. Spatial-temporal Deep Regression Model for Multi-granularity Traffic Flow Prediction[J]. 2023, 40(4): 1-8. DOI： 10.12052/gdutxb.220157.

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。

Abstract

Traffic flow prediction is an important problem in the field of intelligent transportation systems. Most existing traffic-flow prediction methods have made good progress

which however still face the following two key challenges. (1) The underlying pattern of traffic flow depends on not only the historical information along the timeline

but also the information of spatially adjacent areas

making it a challenging problem on how to balance the two temporal-spatial patterns; (2) Due to fact that time information has the characteristic of multiple granularity

such as hour

day and week

how to capture the multi-grained temporal patterns is another challenge problem. In this paper

we design a multi-grained spatio-temporal deep regression model (MGSTDR) to address the above challenges. By extending the typical autoregressive integrated moving average model (ARIMA) on the basis of multi-grained spatio-temporal traffic flow information

the proposed model can effectively use historical information along the timeline as well as the information of adjacent regions

such that the prediction of multi-grained spatio-temporal traffic flow can be performed. Experimental results on several datasets demonstrate that the proposed model outperforms existing benchmark methods on the task of multi-granularity

and particularly obtains an approximately 5.66% improvement in the hourly traffic flow prediction.

关键词

Keywords

references

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DU S D, LI T R, YANG Y, et al . A sequence-to-sequence spatial-temporal attention learning model for urban traffic flow prediction [J ] . Journal of Computer Research and Development, 2020, 57(8): 1715-1728.

MA C, KANG P, LIU X. Hierarchical gating networks for sequential recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery Data Mining. New York: Association for Computing Machinery, 2019: 825-833.

MA C, DAI G, ZHOU J. Short-term traffic flow prediction for urban road sections based on time series analysis and LSTM_BILSTM method [J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5615-5624.

WANG K, SHENG L, GU S, et al . Sequential point cloud upsampling by exploiting multi-scale temporal dependency [J ] . IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31(12): 4686-4696.

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