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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (8): 1607-1617    DOI: 10.3785/j.issn.1008-973X.2023.08.013
    
Prediction of urban interrupted traffic flow based on optimal convergence time interval
Dian-hai WANG(),Rui XIE,Zheng-yi CAI*()
Intelligent Transportation Research Institute, Zhejiang University, Hangzhou 310058, China
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Abstract  

An urban discontinuous traffic flow prediction method based on optimal convergence time interval was proposed, aiming at the discontinuity, periodicity and randomness of urban traffic flow affected by signal control. Firstly, the signal control period of urban discontinuous traffic flow was obtained based on Fourier transform and autocorrelation analysis, and then cross validation mean square error model was used to determine the relationship between optimal convergence time interval and signal period. A LSTM_BConv prediction model combining Bayesian neural network and deep learning model was proposed based on the previous analysis. Experimental results show that: 1) Traffic flow data statistics based on optimal convergence time interval can effectively improve the prediction accuracy of urban discontinuous traffic flow prediction model; 2) The optimal convergence time interval of urban discontinuous traffic flow data is a multiple of the traffic signal control cycle; 3) Comparison test results show that LSTM_BConv model is superior to common benchmark models, and the average absolute percentage error is increased by 4.57%. The prediction results can provide reference for the optimization of signal control scheme.

Key words： urban interrupted flow      optimal convergence time interval      signal control cycle      short term traffic flow prediction      Bayesian convolutional neural network     
Received: 03 December 2022      Published: 31 August 2023
CLC:  U 491  
Fund:  国家自然科学基金资助项目(52131202, 71901193, 52072340); 浙江省"尖兵""领雁"研发攻关计划资助项目（2023C01240, 2023C03155)
Corresponding Authors: Zheng-yi CAI     E-mail: wangdianhai@zju.edu.cn;caizhengyi@zju.edu.cn
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Dian-hai WANG
Rui XIE
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Cite this article:

Dian-hai WANG,Rui XIE,Zheng-yi CAI. Prediction of urban interrupted traffic flow based on optimal convergence time interval. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1607-1617.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.08.013     OR     https://www.zjujournals.com/eng/Y2023/V57/I8/1607


基于最优汇集时间间隔的城市间断交通流预测

针对城市交通流受信号控制的影响而呈现出间断性、周期性和随机性的特点，提出基于最优汇集时间间隔的城市间断交流预测方法. 该方法首先基于傅里叶变换和自相关分析获取城市间断交通流的信号控制周期，再利用交叉验证均方差模型确定最优汇集时间间隔与信号周期的关系，在此基础上提出融合贝叶斯神经网络和深度学习模型的LSTM-BConv预测模型. 基于实测数据的实验结果表明：1）基于最优汇集时间间隔统计交通流数据能有效提升城市间断交通流预测模型的预测精度；2）城市间断交通流数据的最优汇集时间间隔为交通信号控制周期的倍数；3）对比试验结果表明，LSTM-BConv预测模型优于常见的预测模型，平均绝对百分比误差提升了4.57%. 预测结果可以为信号控制方案的优化提供参考依据.


关键词： 城市间断流,  最优汇集时间间隔,  信号控制周期,  短时交通流预测,  贝叶斯卷积神经网络 
Fig.1 Change of through traffic flow with time
Fig.2 Calculation process of cross validation mean square error （CVMSE）
Fig.3 Correlation of different turning traffic flow at intersections
Fig.4 Traditional neural network structure
Fig.5 Bayesian neural network structure
Fig.6 Convolution process of Bayesian convolution neural network
Fig.7 LSTM_ BConv forecasting model framework
Fig.8 Framework diagram of urban interrupted traffic flow prediction based on optimal convergence time interval
Fig.9 Real geographical location distribution of target intersections
Fig.10 Fourier transform amplitude of target intersection
Fig.11 Auto correlation of target intersection at different lags
Fig.12 Actual signal control cycle of target intersection in different periods
Fig.13 CVMSE of target intersection at different convergence intervals
Fig.14 Prediction accuracy MAPE of LSTM_BConv model at intersection 1
Fig.15 Prediction accuracy MAPE of LSTM_BConv model at intersection 2
Fig.16 Comparison of prediction MAPE values of prediction model under different collection intervals
转向 统计学 机器学习 深度学习
HA ARIMA SVR KNN CapsNet GRU STAWnet LSTM_BConv
S_L 3.35 3.97 3.24 3.30 3.36 3.12 3.04 3.06
S_S 7.81 11.35 7.54 8.06 9.87 7.30 7.92 7.23
N_L 3.08 3.53 2.98 3.09 3.01 2.93 2.89 2.87
N_S 9.59 13.75 9.26 9.53 12.43 8.65 9.48 8.61
W_L 3.10 4.25 3.20 3.30 4.05 3.06 3.02 3.01
W_S 1.72 1.92 1.69 1.74 1.81 1.60 1.64 1.58
E_L 2.23 2.26 2.15 2.19 2.14 2.05 2.07 2.03
E_S 3.91 3.87 3.60 3.72 3.69 3.48 3.62 3.41
Mean 4.35 5.61 4.21 4.37 5.05 4.02 4.21 3.98
Tab.1 Comparison of MAE predicted by different prediction models at intersection 1
转向 统计学 机器学习 深度学习
HA ARIMA SVR KNN CapsNet GRU STAWnet LSTM_BConv
S_L 4.24 4.91 4.09 4.15 4.21 3.91 3.84 3.82
S_S 10.25 14.24 9.85 10.37 12.20 9.47 10.09 9.36
N_L 3.85 4.37 3.71 3.83 3.71 3.64 3.62 3.57
N_S 12.27 16.74 11.84 12.09 15.04 11.21 12.23 11.19
W_L 3.98 5.26 4.07 4.20 5.16 3.94 3.89 3.88
W_S 2.09 2.39 2.04 2.10 2.17 1.99 2.00 1.94
E_L 2.76 2.83 2.63 2.70 2.62 2.51 2.53 2.48
E_S 4.75 4.80 4.39 4.55 4.49 4.23 4.41 4.17
Mean 5.52 6.94 5.33 5.50 6.20 5.11 5.33 5.05
Tab.2 Comparison of RMSE predicted by different prediction models at intersection 1
转向 统计学 机器学习 深度学习
HA ARIMA SVR KNN CapsNet GRU STAWnet LSTM_BConv
S_L 26.17 32.36 25.63 26.17 24.09 24.50 24.17 21.61
S_S 15.83 26.23 13.65 14.63 18.63 13.14 14.23 12.67
N_L 32.72 38.89 35.06 36.10 31.72 33.14 34.10 28.45
N_S 19.57 30.61 17.47 17.87 24.16 16.61 17.59 15.54
W_L 29.64 43.02 36.63 38.32 43.90 32.73 34.00 29.18
W_S 41.02 44.29 60.60 64.07 45.96 54.29 39.59 36.88
E_L 36.77 36.24 42.46 44.31 33.03 39.78 42.65 30.56
E_S 31.27 33.64 30.12 31.45 26.99 28.77 30.44 25.34
Mean 29.12 35.66 32.70 34.12 31.06 30.37 29.60 25.03
Tab.3 Comparison of MAPE predicted by different prediction models at intersection 1
Fig.17 Comparison results of ablation experiment
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