考虑动态空间关系的短时交通流预测方法

赵振兴; 曾伟; 唐晨嘉

doi:10.3963/j.jssn.1674-4861.2023.04.015

考虑动态空间关系的短时交通流预测方法

doi: 10.3963/j.jssn.1674-4861.2023.04.015

华中科技大学人工智能与自动化学院武汉 430074

基金项目:

国家自然科学基金项目 71871100

详细信息

作者简介:
赵振兴（1999—），硕士研究生. 研究方向：智能交通. E-mail：zx_zhao@hust.edu.cn

通讯作者:
曾伟（1968—），博士，副教授. 研究方向：系统工程. E-mail：zengwei@mail.hust.edu.cn

中图分类号: U491.14
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- 被引次数: 0
出版历程
- 收稿日期: 2023-02-10
- 网络出版日期: 2023-11-23

A Short-term Traffic Flow Forecasting Model Considering Dynamic Spatio-temporal Relationship

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

摘要

摘要: 为有效提取交通流的时空特征，提升交通流的预测精度，研究了基于动态时空图卷积网络的短时交通流预测模型（DySTGCN）。DySTGCN不仅实现了对交通流时空维度的信息建模，而且考虑了时间维度信息对空间维度信息的影响，创新性提出了基于时间信息的空间拓扑结构——时变空间图（spatial topology graph，TSG），并设计出了1种能够高效、简便地计算时变空间图的深层网络结构。该结构通过编码、解码方式提取不同节点的交通流数据的相关性特征并实现降噪处理。时变空间图反映了交通网络的实时空间特征，基于交通网络中节点空间位置的稳定空间图（stable spatial graph，SG）反映了交通网络的稳定空间特征。TSG与SG在图卷积过程中共同指导交通流预测，更加准确地刻画了交通流的时空特性，以提高预测精度。为测试模型的预测效果，在2个权威公开数据集上进行实验，结果表明：DySTGCN学习到的时变空间图可以较为准确地反映出不同节点的交通流之间的相关性，在平均绝对误差、均方根误差，以及加权平均绝对百分比误差指标上，比其他时空图卷积网络模型如STGCN、ASTGCN等降低了近13.40%、10.98%、16.72%，充分验证了动态空间关系在短时交通流预测中的重要作用。此外，DySTGCN能够提取交通流的周期性特征，实现了对交通流的连续不间断预测。
- 交通运输系统工程 /
- 短时交通流预测 /
- 时变空间图 /
- 动态图卷积 /
- 时空融合 /
- 交通大数据
Abstract: A traffic flow prediction model based on dynamic spatio-temporal graph convolutional network (DySTGCN) is developed, to effectively extract the spatio-temporal features of traffic flows and improve the accuracy of traffic flow prediction. DySTGCN models not only Spatio-temporal information of traffic flow but also the influence of temporal information on spatial information. Meanwhile, a spatial structure based on temporal information, a time-varying spatial topology graph (TSG), is proposed and a deep neural network structure to efficiently calculate the TSG is designed. The structure extracts correlation features of traffic flow at different nodes and can reduce the noise through encoding and decoding. TSG reflects the real-time spatial feature of the traffic network, a stable spatial graph (SG) based on the spatial position of nodes reflects the stable spatial feature. The TSG and SG jointly guide the traffic flow prediction and depict the spatio-temporal feature more accurately to improve the prediction precision. To test the prediction effect of the model, experiments are carried out on two authoritative public data sets. The results show that TSG learned by DySTGCN can accurately reflect the correlation between traffic flows at different nodes and MAE, RMSE and WMAPE of DySTGCN are 13.40%, 10.98%, and 16.72% lower than other spatio-temporal graph convolutional network models such as STGCN, ASTGCN, verifying that dynamic spatial relation plays an important role in short-term traffic flow prediction fully. Besides, DySTGCN can extract periodic features of traffic flow and achieve continuous and uninterrupted prediction of traffic flow.
- engineering of communications and transportation system /
- short-term traffic flow prediction /
- time-varying spatial topology graph /
- dynamic graph convolution /
- spatio-temporal fusion /
- big traffic data

HTML全文

图 1 DySTGCN框架

Figure 1. Framework of DySTGCN

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图 2 TSG估计器框架

Figure 2. Framework of TG estimator

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图 3 参数选择测试结果

Figure 3. Test results of parameter selection

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图 4 不同模型对于预测时长对预测时长敏感程度

Figure 4. Sensitivity of prediction length for models

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图 5 在2个连续时段上生成的部分节点的时变空间图

Figure 5. TSG of partial nodes generated over two consecutive period

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图 6 不同位置色块对应的2个节点流量变化趋势

Figure 6. Traffic flow trends of two nodes at different location

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图 7 消融实验结果

Figure 7. The results of ablation study

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图 8 模型短期预测结果

Figure 8. Short-term prediction of model

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图 9 7 d的交通流量预测结果

Figure 9. Traffic flow prediction of 7 days

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表 1 数据集格式

Table 1. Format of dataset

数据集	网络节点总数/ 个	连边总数/条	可用总步长/ 个	数据单位
PeMSD4	307	340	16992	辆/5min
PeMSD8	170	277	17856	辆/5min

下载: 导出CSV

表 2 PeMSD4数据集实验结果

Table 2. Result of PeMSD4 dataset

模型	MAE	RMSE	WMAPE/%
HA	30.96±0.00	46.57±0.00	14.54±0.00
SVR	24.91±0.00	39.15±0.00	11.70±0.00
STGCN	22.43±0.61	34.38±0.73	11.06±0.21
ASTGCN	22.28±1.22	34.86±1.87	11.44±1.24
STSGCN	21.27±0.15	33.58±0.24	10.79±0.07
STFGNN	19.20±0.09	31.36±0.20	9.75±0.09
DySTGCN	18.43±0.11	29.86±0.21	8.94±0.09

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表 3 PeMSD8数据集实验结果

Table 3. Result of PeMSD8 dataset

模型	MAE	RMSE	WMAPE/%
HA	24.51±0.00	37.13±0.00	10.42%±0.00
SVR	19.98±0.00	32.70±0.00	8.49%±0.00
STGCN	18.23±0.14	27.69±0.21	8.29%±0.11
ASTGCN	17.86±0.42	27.58±0.48	8.48%±0.92
STSGCN	17.65±0.09	27.08±0.20	8.16%±0.07
STFGNN	16.03±0.09	25.82±0.18	7.89%±0.06
DySTGCN	15.84±0.10	24.46±0.18	7.18%±0.07

下载: 导出CSV

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