TrafficPro：一种针对城市信控路网的路段速度预测框架

doi:10.13229/j.cnki.jdxbgxb.20221386

Abstract

Abstract:

When the traditional deep learning-based models predict link speeds for the entire urban traffic network， they do not consider the proactive feature （signal control information） of traffic flow， and therefore achieve low prediction accuracy. In order to tackle this issue， this paper proposed a link speed prediction framework， based on generative adversarial network and graph neural network. By adopting a proactive and a reactive prediction module， the generator of this framework is able to encode traffic flow and signal control information at the entire network level. The discriminator is then used to increase the generalizability of the prediction outcome. By comparing its performance with traditional time-series and deep learning-based models in real-world traffic circumstances， it is found that the proposed framework achieved less prediction error （3%-5% RMSE drop） than the SOTA model （ASTGCN）.

Key words: transportation planning and management, signalized urban traffic network, traffic speed prediction, generative adversarial network

CLC Number:

U491

Xiao-yue WEN,Guo-min QIAN,Hua-hua KONG,Yue-jie MIU,Dian-hai WANG. TrafficPro： a framework to predict link speeds on signalized urban traffic network[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2214-2222.

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路段ID	时刻	平均速度/（km?h^-1）	信控数据 /s	信控独热编码向量
001	2020-01-01 08：00：00	28.2	30	［0，1，0，…，0］
001	2020-01-01 08：05：00	31.5	30	［0，1，0，…，0］
001	2020-01-01 08：10：00	30.6	30	［0，1，0，…，0］
001	2020-01-01 08：15：00	24.5	30	［0，1，0，…，0］
001	2020-01-01 08：20：00	22.3	25	［1，0，0，…，0］
001	2020-01-01 08：25：00	23.9	25	［1，0，0，…，0］
001	2020-01-01 08：30：00	21.7	25	［1，0，0，…，0］

模型	RMSE	MAE	MAPE/%
HA	8.87	7.61	21.8
SVR	8.63	7.32	16.5
ARIMA	6.02	5.18	14.8
GCN	6.94	6.46	11.6
GRU	6.48	5.34	11.2
T-GCN	5.60	4.42	10.7
ST-GCN	5.27	3.88	9.5
ASTGCN	4.75	3.43	9.1
TrafficPro（G）	4.21	3.11	8.9
TrafficPro	4.05	2.99	8.6

模型	RMSE	MAE	MAPE/%
HA	11.4	9.14	25.7
SVR	10.5	8.72	19.8
ARIMA	7.11	6.79	17.5
GCN	8.54	7.78	13.2
GRU	6.88	5.87	12.4
T-GCN	5.91	4.86	11.5
ST-GCN	5.45	4.41	9.8
ASTGCN	5.06	3.67	9.6
TrafficPro（G）	4.54	3.40	9.4
TrafficPro	4.40	3.21	8.9

模型	RMSE	MAE	MAPE/%
HA	16.5	12.0	33.9
SVR	14.3	11.6	23.5
ARIMA	9.74	9.27	20.1
GCN	9.81	9.19	15.4
GRU	8.53	7.27	14.0
T-GCN	6.52	5.62	12.8
ST-GCN	5.86	4.99	10.9
ASTGCN	5.26	3.83	10.3
TrafficPro（G）	4.84	3.52	9.8
TrafficPro	4.59	3.39	9.3

TrafficPro： a framework to predict link speeds on signalized urban traffic network

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