Traffic flow parameter prediction method based on dynamic graphs self-attention

doi:10.3969/j.issn.1674-8484.2024.05.006

Abstract

Abstract:

In order to improve the driving efficiency and safety of intelligent vehicles in the areas with frequent abnormal events, a traffic flow parameter prediction method was designed based on dynamic node self-attention to improve the accuracy of traffic flow parameter prediction. The spatial attention was used to aggregate the features of neighborhood nodes in multiple time steps, and then the traffic parameters were predicted by the temporal attention mechanism along the time dimension. The results show that the Mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of the one-hour prediction of Dynamic Picture Self-Attention (DGSA) model decrease by 3.75%, 3.45% and 11.63%, respectively. The simulated road average collision time (TTC) is longer, reaching 2.8 s. The proposed method can effectively predict the evolution trend of traffic flow and improve the safety of vehicles under abnormal events.

Key words: intelligent vehicles, traffic flow parameter prediction, abnormal events, dynamic graphs, deep learning

CLC Number:

TP391

SHI Tianjing, LI Xu. Traffic flow parameter prediction method based on dynamic graphs self-attention[J]. Journal of Automotive Safety and Energy, 2024, 15(5): 680-688.

Figures/Tables 10

References 27

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预测模型		MAE			RMSE			MAPE / %
预测模型	15 min	30 min	60 min	15 min	30 min	60 min	15 min	30 min	60 min
ARIMA	2.369 4	2.660 4	3.737 0	4.149 7	5.004 7	7.300 5	4.890 2	5.418 9	6.413 7
LSTM	2.056 7	2.139 2	2.881 7	3.893 1	4.557 3	6.378 7	4.147 5	4.736 9	6.070 8
GRU	1.717 4	2.071 3	3.177 2	3.426 3	4.455 4	6.700 7	3.526 6	4.190 2	6.294 2
ST-GCN	1.983 6	2.330 2	3.069 8	3.820 5	4.348 4	6.082 0	3.193 6	3.912 7	5.513 7
WaveNet	1.964 3	2.283 1	2.761 4	3.367 4	4.002 6	5.620 3	3.105 2	3.803 2	5.127 5
DGSA	1.671 0	1.946 2	2.657 9	3.104 5	3.771 2	5.426 3	2.713 4	3.448 9	4.531 0

预测模型		MAE			RMSE			MAPE / %
预测模型	15 min	30 min	60 min	15 min	30 min	60 min	15 min	30 min	60 min
ARIMA	2.369 4	2.660 4	3.737 0	4.149 7	5.004 7	7.300 5	4.890 2	5.418 9	6.413 7
LSTM	2.056 7	2.139 2	2.881 7	3.893 1	4.557 3	6.378 7	4.147 5	4.736 9	6.070 8
GRU	1.717 4	2.071 3	3.177 2	3.426 3	4.455 4	6.700 7	3.526 6	4.190 2	6.294 2
ST-GCN	1.983 6	2.330 2	3.069 8	3.820 5	4.348 4	6.082 0	3.193 6	3.912 7	5.513 7
WaveNet	1.964 3	2.283 1	2.761 4	3.367 4	4.002 6	5.620 3	3.105 2	3.803 2	5.127 5
DGSA	1.671 0	1.946 2	2.657 9	3.104 5	3.771 2	5.426 3	2.713 4	3.448 9	4.531 0

预测模型	TTC / s
ARIMA	1.3
LSTM	1.9
GRU	2.6
ST-GCN	2.2
WaveNet	2.3
DGSA	2.8

预测模型	TTC / s
ARIMA	1.3
LSTM	1.9
GRU	2.6
ST-GCN	2.2
WaveNet	2.3
DGSA	2.8