混合策略改进WOA-BiLSTM的快速路出口匝道车速预测

doi:10.13229/j.cnki.jdxbgxb.20230940

Abstract

Abstract:

Aiming at the problem that the existing meta-heuristic algorithm has slow convergence speed and large error in the process of optimizing the neural network to predict vehicle speed， a vehicle speed prediction method for expressway off-ramp based on IWOA-BiLSTM was proposed. Firstly， the Circle chaotic map was used to replace the randomly generated initial population in the whale optimization algorithm to increase the diversity and quality of the population.Secondly， the elite opposition-based learning strategy was used to improve the diversity of the individual 's preferred position and reduce the risk of the algorithm falling into local optimum and premature convergence.Finally， the cosine function was used to change the adaptive convergence factor and introduce the inertia weight strategy. On the premise of retaining the advantages of the whale optimization algorithm， the global search and local development capabilities of the algorithm were balanced. The simulation results show that， compared with the existing meta-heuristic algorithm and vehicle speed prediction model， the IWOA algorithm has significantly improved in terms of optimization accuracy， convergence rate and prediction accuracy.

Key words: transportation planning and management, exit ramp, speed prediction, whale optimization algorithm（WOA）, hybrid strategy, bidirectional long short-term memory（BiLSTM）

CLC Number:

U491

Qing-ling HE,Yu-long PEI,Lin HOU,Jing LIU,Sheng PAN. Hybrid strategy improves WOA⁃BiLSTM speed prediction of expressway exit ramp[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(6): 2038-2049.

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	交通流量	平均车速	正常驾驶	强行换道	频繁换道	缓速行驶	近距跟驰	急加减速
交通流量	交通流量	***	***	***	***	***	***	***
平均车速	0.17	平均车速	***	***	***	***	***	***
正常驾驶	0.52	0.63	正常驾驶	***	***	***	***	***
强行换道	-0.49	-0.56	-0.78	强行换道	***	***	***	***
频繁换道	0.18	0.30	0.22	-0.26	频繁换道	***	***	**
缓速行驶	-0.49	-0.62	-0.84	0.64	-0.41	缓速行驶	***
近距跟驰	-0.22	-0.27	-0.32	0.37	-0.13	0.24	近距跟驰	***
急加减速	-0.092	-0.14	-0.16	0.19	-0.097	0.081	0.13	急加减速

测试函数	指标	IWOA	WOA	GWO	PSO	GA
f₁	平均值	2.6E-72	1.1E-50	1.0E-16	1.6E+01	3.9E+01
f₁	标准差	1.4E-71	4.7E-50	6.6E-17	7.1E+00	6.7E+00
f₂	平均值	3.0E-05	6.2E-01	4.3E+04	1.0E+04	3.8E+04
f₂	标准差	9.4E-05	2.3E+00	1.8E+04	6.5E+03	8.5E+03
f₃	平均值	2.1E-23	4.8E+01	8.2E-07	9.5E+00	6.8E+01
f₃	标准差	9.1E-23	3.1E+01	9.5E-07	2.9E+00	8.6E+00
f₄	平均值	7.4E-05	4.1E-01	7.0E-01	3.3E+02	9.5E+03
f₄	标准差	1.1E-04	2.6E-01	3.4E-01	1.5E+02	5.7E+03
f₅	平均值	-1.2E+04	-1.0E+04	-6.0E+03	-6.8E+03	-2.1E+03
f₅	标准差	1.1E+03	1.9E+03	6.8E+02	1.1E+03	4.1E+02
f₆	平均值	1.9E-15	0.0E+00	2.7E+00	2.0E+02	2.6E+02
f₆	标准差	1.0E-14	0.0E+00	2.9E+00	2.9E+01	3.3E+01
f₇	平均值	0.0E+00	1.5E-02	5.0E-03	4.4E+00	1.1E+02
f₇	标准差	0.0E+00	4.6E-02	1.0E-02	1.4E+00	6.7E+01
f₈	平均值	4.7E-03	2.7E-02	4.6E-02	5.8E+00	8.4E+00
f₈	标准差	2.5E-02	2.7E-02	2.5E-02	2.5E+00	4.2E+00
f₉	平均值	6.5E-04	4.3E-03	6.4E-03	1.2E-02	8.7E-03
f₉	标准差	4.2E-04	1.2E-02	9.9E-03	9.2E-03	9.9E-03
f₁₀	平均值	-3.3E+00	-3.2E+00	-2.6E+00	-3.1E+00	-1.6E+00
f₁₀	标准差	7.0E-02	1.3E-01	7.9E-01	1.7E-01	5.0E-01
f₁₁	平均值	-9.6E+00	-7.5E+00	-8.3E+00	-8.0E+00	-2.2E+00
f₁₁	标准差	1.6E+00	3.1E+00	2.5E+00	2.5E+00	8.0E-01
f₁₂	平均值	-1.0E+01	-1.0E+01	-8.7E+00	-6.7E+00	-2.4E+00
f₁₂	标准差	9.9E-01	2.1E+00	2.9E+00	3.0E+00	9.2E-01

函数	平均耗时		WOA与IWOA平均耗时的百分比/%
函数	WOA	IWOA	WOA与IWOA平均耗时的百分比/%
单峰基准测试函数	1.570	1.828	116.43
多峰基准测试函数	1.977	1.905	96.36
复合基准测试函数	1.701	1.694	99.59
平均值	1.749	1.809	103.41

算法	MAE	排名
IWOA	2.13E+01	1
WOA	1.78E+02	2
PSO	1.40E+03	3
GWO	4.10E+03	4
GA	4.85E+03	5

类别	5 min			15 min			30 min
类别	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%	MAE	RMSE	MAPE/%
IWOA-BiLSTM	4.7	5.0	7.5	4.6	4.8	7.4	4.5	4.6	7.2
WOA-BiLSTM	7.0	7.2	11.5	6.9	7.2	11.3	6.8	6.8	11.0
PSO-ELMAN	7.7	7.8	12.5	7.2	7.3	11.7	7.1	7.3	11.6
GA-BP	8.4	8.3	13.5	7.7	7.5	12.4	7.3	7.3	11.8
LSTM	7.8	7.9	12.8	7.6	7.9	12.5	7.6	7.4	12.3
SVM	8.9	9.2	14.6	8.3	8.9	13.7	7.9	8.0	12.9

Hybrid strategy improves WOA⁃BiLSTM speed prediction of expressway exit ramp

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