改进鲸鱼优化GRU的窄路短时车流量预测

doi:10.19678/j.issn.1000-3428.0068510

摘要/Abstract

摘要：

窄路段作为交通场景中不可避免的瓶颈路段, 其短时车流量预测对优化路径规划、改善交通状况具有重要意义。针对窄路段的时效性, 同时考虑适用模型的准确度, 提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化算法(IWOA)-门控循环单元(GRU)的窄路短时车流量预测模型, 以SUMO(Simulation of Urban Mobility)仿真数据进行了实证研究。对比实验结果显示, IWOA具有较好的全局性、收敛速度且更加稳定。基于IWOA-GRU的窄路短时车流量预测模型, 均方根误差(RMSE)指标相较于WOA-GRU、PSO-GRU、长短期记忆神经(LSTM)网络分别降低10.96%、28.71%、42.23%, 平均绝对百分比误差(MAPE)指标分别降低13.92%、46.18%、52.83%, 有较为显著的准确性和稳定性。

关键词: 短时车流量预测, 窄路段, 鲸鱼优化算法, 门控循环单元, SUMO软件

Abstract:

To address the unavoidable bottleneck in traffic scenes, the short-time traffic flow prediction of narrow roads is very important for optimizing path planning and improving traffic conditions. For traffic on narrow roads, an Improved Whale Optimization Algorithm (IWOA)-Gated Recurrent Unit (GRU) short-time narrow road traffic prediction model is proposed based on a good node-set initialization population, nonlinear parameter control, and Cauchy variation perturbation. An empirical study is conducted using the Simulation of Urban Mobility (SUMO) dataset. The experimental results show that IWOA has better global performance, convergence speed, and stability compared with WOA-GRU, Particle Swarm Optimization (PSO)-GRU, and Long Short-Term Memory (LSTM). The Root Mean Square Error (RMSE) of the proposed method decreased by 10.96%, 28.71%, and 42.23%, respectively, and Mean Absolute Percentage Error (MAPE) decreased by 13.92%, 46.18%, and 52.83%, respectively, indicating significant accuracy and stability.

Key words: short-time traffic flow prediction, narrow roads, Whale Optimization Algorithm (WOA), Gated Recurrent Unit (GRU), Simulation of Urban Mobility (SUMO) software

贾硕, 林士飏, 杨苗会, 孙滕. 改进鲸鱼优化GRU的窄路短时车流量预测[J]. 计算机工程, 2025, 51(2): 111-125.

JIA Shuo, LIN Shiyang, YANG Miaohui, SUN Teng. Short-Time Traffic Flow Prediction on Narrow Roads Based on Improved Whale-Optimized GRU[J]. Computer Engineering, 2025, 51(2): 111-125.

https://www.ecice06.com/CN/Y2025/V51/I2/111

图/表 23

图1 窄路通行会车场景

Fig.1 Narrow roads traffic meeting scene

图2 IWOA-GRU预测模型流程图

Fig.2 Flow chart of IWOA-GRU prediction model

图3 部分单模态基准函数收敛曲线

Fig.3 Convergence curve of partial unimodal reference function

图4 部分多模态基准函数收敛曲线

Fig.4 Convergence curve of partial multimodal reference function

图5 部分固定维多模态基准函数收敛曲线

Fig.5 Convergence curve of partial fixed dimensional multimodal reference function

图6 部分单模态基准函数箱线图

Fig.6 Box diagram of partial unimodal reference function

图7 部分多模态基准函数箱线图

Fig.7 Box diagram of partial multimodal reference function

图8 部分固定维多模态基准函数箱线图

Fig.8 Box diagram of partial fixed dimensional multimodal reference function

图9 4种算法的运行时间

Fig.9 Running time of four algorithms

图10 路网示意图

Fig.10 Road network diagram

图11 窄路车流量时间序列

Fig.11 Narrow road traffic flow time series

图12 车流量预测结果对比

Fig.12 Comparison of traffic flow prediction results

图13 预测相对误差对比

Fig.13 Comparison of prediction relative error

参考文献 32

1	VLAHOGIANNI E I , KARLAFTIS M G , GOLIAS J C . Short-term traffic forecasting: where we are and where we're going. Transportation Research Part C: Emerging Technologies, 2014, 43, 3- 19. doi: 10.1016/j.trc.2014.01.005
2	LEVIN M , TSAO Y D . On forecasting freeway occupancies and volumes(abridgment). Transportation Research Record, 1980, (773): 47- 49.
3	WILLIAMS B M , HOEL L A . Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. Journal of Transportation Engineering, 2003, 129 (6): 664- 672. doi: 10.1061/(ASCE)0733-947X(2003)129:6(664)
4	GUO J H , HUANG W , WILLIAMS B M . Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C: Emerging Technologies, 2014, 43, 50- 64. doi: 10.1016/j.trc.2014.02.006
5	KAMARIANAKIS Y , PRASTACOS P . Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1857 (1): 74- 84. doi: 10.3141/1857-09
6	OKUTANI I , STEPHANEDES Y J . Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B: Methodological, 1984, 18 (1): 1- 11. doi: 10.1016/0191-2615(84)90002-X
7	ERMAGUN A , LEVINSON D . Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending. Transportation Research Part C: Emerging Technologies, 2019, 104, 38- 52. doi: 10.1016/j.trc.2019.04.014
8	WANG J , DENG W , GUO Y T . New Bayesian combination method for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 2014, 43, 79- 94. doi: 10.1016/j.trc.2014.02.005
9	QI Y , ISHAK S . A hidden Markov model for short term prediction of traffic conditions on freeways. Transportation Research Part C: Emerging Technologies, 2014, 43, 95- 111. doi: 10.1016/j.trc.2014.02.007
10	CASTRO-NETO M , JEONG Y S , JEONG M K , et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 2009, 36 (3): 6164- 6173. doi: 10.1016/j.eswa.2008.07.069
11	WANG J , SHI Q X . Short-term traffic speed forecasting hybrid model based on Chaos-wavelet analysis-support vector machine theory. Transportation Research Part C: Emerging Technologies, 2013, 27, 219- 232. doi: 10.1016/j.trc.2012.08.004
12	ZHENG Z D , SU D C . Short-term traffic volume forecasting: a k-nearest neighbor approach enhanced by constrained linearly sewing principle component algorithm. Transportation Research Part C: Emerging Technologies, 2014, 43, 143- 157. doi: 10.1016/j.trc.2014.02.009
13	VLAHOGIANNI E I , KARLAFTIS M G , GOLIAS J C . Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transportation Research Part C: Emerging Technologies, 2005, 13 (3): 211- 234. doi: 10.1016/j.trc.2005.04.007
14	DUNNE S , GHOSH B . Regime-based short-term multivariate traffic condition forecasting algorithm. Journal of Transportation Engineering, 2012, 138 (4): 455- 466. doi: 10.1061/(ASCE)TE.1943-5436.0000337
15	LV Y S , DUAN Y J , KANG W W , et al. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 2014, 16 (2): 865- 873. URL
16	LA A I , LOBO J L , CAPECCI E , et al. Adaptive long-term traffic state estimation with evolving spiking neural networks. Transportation Research Part C: Emerging Technologies, 2019, 101, 126- 144. doi: 10.1016/j.trc.2019.02.011
17	MA X L , TAO Z M , WANG Y H , et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 2015, 54, 187- 197. doi: 10.1016/j.trc.2015.03.014
18	ZHENG W Z , LEE D H , SHI Q X . Short-term freeway traffic flow prediction: Bayesian combined neural network approach. Journal of Transportation Engineering, 2006, 132 (2): 114- 121. doi: 10.1061/(ASCE)0733-947X(2006)132:2(114)
19	DIMITRIOU L , TSEKERIS T , STATHOPOULOS A . Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow. Transportation Research Part C: Emerging Technologies, 2008, 16 (5): 554- 573. doi: 10.1016/j.trc.2007.11.003
20	罗例东. 高速公路异常事件影响范围演化分析与预测研究[D]. 重庆: 重庆大学, 2015.
	LUO L D. Evolution analysis and prediction research on the influence range of expressway abnormal events[D]. Chongqing: Chongqing University, 2015. (in Chinese)
21	张小安. 交通事故条件下的交通流仿真研究[D]. 广州: 广州大学, 2017.
	ZHANG X A. Research on traffic flow simulation under traffic accident conditions[D]. Guangzhou: Guangzhou University, 2017. (in Chinese)
22	王伟, 安晖, 宋娟, 等. 从百度、特斯拉最新成果看智能网联汽车发展趋势[N]. 中国计算机报, 2021-11-08(14). 10.3969/j.issn.2096-0182.2021.05.005
	WANG W, AN H, SONG J, et al. From Baidu, Tesla made the latest achievements in the intelligence development trend of automobile[N]. China Computer News, 2021-11-08(14). (in Chinese) 10.3969/j.issn.2096-0182.2021.05.005
23	孙勇义. Apollo开放的自动驾驶之路. 软件和集成电路, 2017, (11): 78- 79. doi: 10.3969/j.issn.2096-062X.2017.11.039
	SUN Y Y . Apollo's open road to autonomous driving. Software and Integrated Circuit, 2017, (11): 78- 79. doi: 10.3969/j.issn.2096-062X.2017.11.039
24	张明. 蘑菇车联发布以车路云为核心的自动驾驶系统. 工业经济论坛, 2020, 7 (6): 80- 81.
	ZHANG M . Mushroom Car Federation released an automatic driving system with vehicle-road-cloud as the core. Intelligent Connected Vehicles, 2020, 7 (6): 80- 81.
25	沈记全, 魏坤. 融合残差网络的CR-BiGRU入侵检测模型. 吉林大学学报(理学版), 2023, 61 (2): 353- 361. doi: 10.13413/j.cnki.jdxblxb.2022032
	SHEN J Q , WEI K . CR-BiGRU intrusion detection model based on residual network. Journal of Jilin University (Science Edition), 2023, 61 (2): 353- 361. doi: 10.13413/j.cnki.jdxblxb.2022032
26	CHUNG J, GVLÇEHRE Ç, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL]. [2023-08-10]. https://arxiv.org/abs/1412.3555.
27	GOODFELLOW I , BENGIO Y , COURVILLE A , et al. Deep learning: adaptive computation and machine learning series. Cambridge, USA: The MIT Press, 2016.
28	MIRJALILI S , LEWIS A . The whale optimization algorithm. Advances in Engineering Software, 2016, 95, 51- 67. doi: 10.1016/j.advengsoft.2016.01.008
29	SYAFARUDDIN , NARIMATSU H , MIYAUCHI H . Optimal energy utilization of photovoltaic systems using the non-binary genetic algorithm. Energy Technology & Policy, 2015, 2 (1): 10- 18. URL
30	华罗庚, 王元. 数论在近似分析中的应用. 北京: 科学出版社, 1978.
	HUA L G , W Y . Application of number theory in approximate analysis. Beijing: Science Press, 1978.
31	OU X F , WU M , PU Y Y , et al. Cuckoo search algorithm with fuzzy logic and Gauss-Cauchy for minimizing localization error of WSN. Applied Soft Computing, 2022, 125, 109211. doi: 10.1016/j.asoc.2022.109211
32	LOPEZ P A, BEHRISCH M, BIEKER-WALZ L, et al. Microscopic traffic simulation using SUMO[C]//Proceedings of the 21st International Conference on Intelligent Transportation Systems. Washington D. C., USA: IEEE Press, 2018: 2575-2582.

[1]	郑雅洲, 刘万平, 黄东. 一种基于注意力机制的BERT-CNN-GRU检测方法[J]. 计算机工程, 2025, 51(1): 258-268.
[2]	杨明强, 卢健. 基于跨模态注意力的目标语音提取[J]. 计算机工程, 2024, 50(9): 121-129.
[3]	赵博超, 马嘉骏, 崔磊, 栾文鹏, 朱静. 基于改进VMD-XGBoost-BiLSTM组合模型的光伏发电异常检测[J]. 计算机工程, 2024, 50(3): 306-316.
[4]	丁国辉, 刘宇琪, 王言开, 耿施展, 姜天昊. 基于翻转网络的低相关性序列数据预测研究[J]. 计算机工程, 2024, 50(2): 78-90.
[5]	史昕, 曹凤腾, 纪艺, 马峻岩. 基于多尺度时空特征与软注意力机制的交通流预测方法[J]. 计算机工程, 2024, 50(12): 346-357.
[6]	郭尚伟, 刘树峰, 李子铭, 欧阳德强, 王宁, 向涛. 基于融合模型的网络安全态势感知方法[J]. 计算机工程, 2024, 50(11): 1-9.
[7]	张晓晖, 马慧芳, 王文涛, 高子皓. 基于跨会话知识图谱的图注意力网络推荐方法[J]. 计算机工程, 2023, 49(2): 136-142,149.
[8]	邓昕, 刘朝晖, 欧阳燕, 陈建华. 基于CNN CBAM-BiGRU Attention的加密恶意流量识别[J]. 计算机工程, 2023, 49(11): 178-186.
[9]	郭可翔, 王衡军, 白祉旭. 融合多通道CNN与BiGRU的字词级文本错误检测模型[J]. 计算机工程, 2022, 48(9): 63-70.
[10]	田乔鑫, 孔韦韦, 滕金保, 王照乾. 基于并行混合网络与注意力机制的文本情感分析模型[J]. 计算机工程, 2022, 48(8): 266-273.
[11]	王曙燕, 原柯. 基于RoBERTa-WWM的大学生论坛情感分析模型[J]. 计算机工程, 2022, 48(8): 292-298,305.
[12]	杜清华, 张凯. 一种高效的跨平台工作流优化方法[J]. 计算机工程, 2022, 48(7): 13-21,28.
[13]	李军怀, 陈苗苗, 王怀军, 崔颖安, 张爱华. 基于ALBERT-BGRU-CRF的中文命名实体识别方法[J]. 计算机工程, 2022, 48(6): 89-94,106.
[14]	陈可嘉, 刘惠. 基于改进BiGRU-CNN的中文文本分类方法[J]. 计算机工程, 2022, 48(5): 59-66,73.
[15]	贺小伟, 徐靖杰, 王宾, 吴昊, 张博文. 基于GRU-LSTM组合模型的云计算资源负载预测研究[J]. 计算机工程, 2022, 48(5): 11-17,34.

选择文件类型/文献管理软件名称

选择包含的内容