一种融合纵横时空特征的交通流预测方法

doi:10.19665/j.issn1001-2400.20221101

Abstract

Abstract:

Aiming at the problems of insufficient mining of time delay characteristics and spatial flow characteristics of upstream and downstream traffic flow as well as insufficient consideration of spatiotemporal characteristics of lane-level traffic flow in existing urban road traffic flow prediction research,a traffic flow prediction method for integrating longitudinal and horizontal spatiotemporal characteristics is proposed.First,the method quantifies and eliminates the effect of spatial time lag between upstream and downstream traffic flow by calculating the delay time to enhance the spatiotemporal correlation of upstream and downstream traffic flow sequences.Then,the traffic flow with the elimination of spatial time lag is passed into the bidirectional long short-term memory network through the vector split data input method to capture the longitudinal transmission and backtracking bidirectional spatiotemporal relationship of upstream and downstream traffic flow.At the same time,the multiscale convolution group is used to mine the multi-time step horizontal spatiotemporal relationship between the traffic flows of each lane in the section to be predicted.Finally,the attention mechanism is used to dynamically fuse the longitudinal and horizontal spatiotemporal characteristics to obtain the predicted value.Experimental results show that by applying the proposed method in the single-step prediction experiment,the MAE and RMSE decrease by 15.26% and 13.83% respectively,and increase by 1.25% compared with conventional time series prediction model.In the medium and long-term multi-step prediction experiment,it is further proved that the proposed method can effectively mine the fine-grained spatiotemporal characteristics of longitudinal and horizontal traffic flow,and has a certain stability and universality.

Key words: urban transportation, traffic flow prediction, longitudinal and horizontal spatiotemporal correlation, deep learning, feature fusion

CLC Number:

U491

HOU Yue,ZHENG Xin,HAN Chengyan. Traffic flow prediction method for integrating longitudinal and horizontal spatiotemporal characteristics[J].Journal of Xidian University, 2023, 50(5): 65-74.

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References 17

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车道	车道1	车道2	车道3	车道4	车道5
车道1	1.000	0.679	0.642	0.658	0.447
车道2	0.679	1.000	0.815	0.811	0.532
车道3	0.642	0.815	1.000	0.926	0.552
车道4	0.658	0.811	0.928	1.000	0.598
车道5	0.447	0.532	0.552	0.598	1.000

模型	R_MAE	R_RMSE	R²
LSTM	4.633	6.458	0.953 60
Bi-LSTM	4.549	6.333	0.955 38
Bi-LSTM+纵向时空特征	4.344	6.195	0.957 30
LSTM+纵横时空特征	4.318	6.101	0.958 59
Bi-LSTM+纵横时空特征	4.190	5.940	0.960 75
Bi-LSTM-MConv	3.973	5.658	0.964 39
Bi-LSTM-MConv-Attention	3.926	5.565	0.965 55

Traffic flow prediction method for integrating longitudinal and horizontal spatiotemporal characteristics

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References 17

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