This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

中文翻译：

---

单向和双向LSTM模型用于短期流量预测

本文介绍了使用单向和双向深度学习长短期记忆（LSTM）神经网络的短期交通预测模型的开发和评估。单向LSTM（Uni-LSTM）模型通过识别较长时间的交通时间序列数据序列的能力而提供了高性能。在这项工作中，Uni-LSTM扩展到双向LSTM（BiLSTM）网络，该网络通过正向和反向两次训练输入数据。本文使用从澳大利亚多条高速公路收集的公共现场观测数据集，对这两种模型的短期速度和交通流量预测进行了比较评估。结果表明，BiLSTM在速度和流量的可变预测范围内表现更好。还针对15分钟的预测范围对堆叠和混合的Uni-LSTM和BiLSTM模型进行了研究，从而在使用4层BiLSTM网络时提高了准确性。然后使用来自三个不同高速公路的数据对优化的4层BiLSTM模型进行校准和验证，以用于多个预测范围。验证结果表明，对于高达60分钟的预测范围的速度，其预测准确度超过90％。对于流量，该模型在5分钟和10分钟的预测范围内达到了90％以上的精度，在15分钟和30分钟的预测范围内达到了80％以上的精度。这些发现扩展了可供道路运营商使用的AI模型集，并使他们有信心应用经过澳大利亚不同高速公路测试和评估的强大模型。