Traffic flow prediction using bi-directional gated recurrent unit method

Abstract

Traffic flow prediction plays an important role in intelligent transportation systems. To accurately capture the complex non-linear temporal characteristics of traffic flow, this paper adopts a Bi-directional Gated Recurrent Unit (Bi-GRU) model in traffic flow prediction. Compared to Gated Recurrent Unit (GRU), which can memorize information from the previous sequence, this model can memorize the traffic flow information in both previous and subsequent sequence. To demonstrate the model’s performance, a set of real case data at 1-hour intervals from 5 working days was used, wherein the dataset was separated into training and validation. To improve data quality, an augmented dickey-fuller unit root test and differential processing were performed before model training. Four benchmark models were used, including the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and GRU. The prediction results show the superior performance of Bi-GRU. The Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of the Bi-GRU model are 30.38, 9.88%, and 23.35, respectively. The prediction accuracy of LSTM, Bi-LSTM, GRU, and Bi-GRU, which belong to deep learning methods, is significantly higher than that of the traditional ARIMA model. The MAPE difference of Bi-GRU and GRU is 0.48% which is a small prediction error value. The results show that the prediction accuracy of the peak period is higher than that of the low peak. The Bi-GRU model has a certain lag on traffic flow prediction.

Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

“…leveraged a hybrid model combining Spatiotemporal Feature Selection Algorithm (STFSA) with a convolutional neural network (CNN) to create a two-dimensional matrix for short-term traffic flow prediction, yielding better accuracy than single models like SVR, SARIMA, KNN, ANN, or even combined models like STFSA-ANN [19]. Wang S. extended this hybrid model concept by integrating STFSA with a gated recurrent unit (GRU), which exhibited substantial improvements over standalone CNN and GRU models in both precision and reliability for shortterm traffic forecasting [20].…”

Artificial Intelligence Applications in Road Traffic Forecasting: A Review of Current Research

“…To address the problem of traffic flow prediction and propose the use of a Bi-GRU model, Wang et al in 2022 used real case data comparison with benchmark models for the evaluation and assessment. The results demonstrate that the Bi-GRU model outperforms other models in terms of prediction accuracy, indicating its effectiveness in capturing the sophisticated non-linear temporal characteristics of traffic flow [22].…”

Traffic Flow Prediction in Urban Networks: Integrating Sequential Neural Network Architectures

“…On the other hand, LSTM performed better with more complex datasets and required the use of extended sequences to predict future traffic volume. A comparative analysis with benchmark models proposed in [18], including ARIMA, LSTM, BiLSTM, and GRU, indicates the superior performance of the BiGRU model. The bidirectional model utilizes preceding and succeeding time sequences to extract additional traffic flow information.…”