Volume 41 Issue 3
Jun.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(3): 166-174
JI Xiaofeng, KONG Xiaoli, CHEN Fang, HAO Jingjing, QIN Wenwen. A Forecasting Model of Short-term Traffic Flow on Expressways During Holidays Based on ETC Data and A-BiLSTM Neural Network Models[J]. Journal of Transport Information and Safety, 2023, 41(3): 166-174. doi: 10.3963/j.jssn.1674-4861.2023.03.018
Citation: JI Xiaofeng, KONG Xiaoli, CHEN Fang, HAO Jingjing, QIN Wenwen. A Forecasting Model of Short-term Traffic Flow on Expressways During Holidays Based on ETC Data and A-BiLSTM Neural Network Models[J]. Journal of Transport Information and Safety, 2023, 41(3): 166-174. doi: 10.3963/j.jssn.1674-4861.2023.03.018
shu

A Forecasting Model of Short-term Traffic Flow on Expressways During Holidays Based on ETC Data and A-BiLSTM Neural Network Models

doi: 10.3963/j.jssn.1674-4861.2023.03.018
  • 1.

    Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China

  • 2.

    Yunnan Modern Logistics Engineering Research Center, Kunming University of Science and Technology, Kunming 650500, China

  • Received Date: 2022-03-29
    Available Online: 2023-09-16
    Abstract
  • Data collected from the electronic toll collection (ETC) gantry system can be used to support the short-term traffic flow forecasting for expressways during holidays. An Attention-BiLSTM (A-BiLSTM) hybrid model, composed of the attention mechanism and bidirectional long/short-term memory (BiLSTM) neural network, is proposed to address the issues of high nonlinearity and complexity within traffic flow forecasting tasks for holidays based on ETC gantry data. The input data is preprocessed to improve the effectiveness of model training. A sliding window method is used to generate samples of supervised learning to improve the efficiency of model training. Forward and backward time-dependent features of traffic flow data is extracted based on the BiLSTM neural network. An attention mechanism is introduced to dynamically weigh the importance of the information extracted from the neural network, enhancing the ability of nonlinear expression of features in hidden layers. A Bayesian optimization method is applied to tune hyperparameters of the model, which can improve the performance of the proposed model. The gantry data is collected from Baihanchang to Lashi on the Dali-Lijang Expressway, and is divided into the data with a time granularities of 5, 10, and 15 min for model development and validation. Experiment results show that: ①Compared with the prediction results of autoregressive moving average (ARIMA) model and support vector machine (SVM) model, the root mean square error (RMSE) of A-BiLSTM hybrid model is reduced by 73.3% and 49.1%, and mean absolute error (MAE) is reduced by 76.0% and 56.3% respectively, which shows that the proposed A-BiLSTM hybrid model has a better prediction capability and can be applied to real-world traffic operation and management. ②Compared with the BiLSTM model without the attention mechanism, the RMSE and MAE of A-BiLSTM hybrid model is reduced by 41.9% and 46.0%, respectively. ③Compared with the models developed using the traffic flow data with a time granularity of 10 and 15 min, the RMSE of the model developed with the data with a time granularity of 5 minutes decreases by 34.5% and 42.1%, respectively; and the MAE decreases by 39.9% and 46.3%, respectively. Therefore, it can be concluded that the A-BiLSTM model performs best with the input data with a time granularity of 5 min.

     

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