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首页> 外文期刊>Arabian Journal for Science and Engineering >Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN
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Data-Driven Short-Term Forecasting for Urban Road Network Traffic Based on Data Processing and LSTM-RNN

机译：基于数据处理和LSTM-RNN的数据驱动的城市路网交通流量短期预测

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摘要

A short-term traffic flow prediction framework is proposed for urban road networks based on data-driven methods that mainly include two modules. The first module contains a set of algorithms to process traffic flow data. After analysis and repair, a complete data set without outliers is provided as well as a data set containing pairs of road segments that are the most similar to each other in regard to their trends. The second module focuses on multiple time-step short-term forecasting. With a good understanding of the periodicity and randomness of traffic flow, the time series is first decomposed into a trend series and residual series. After reconstructing the two time series, model training and prediction based on a long short-term memory-recurrent neural network (LSTM-RNN) are performed. Finally, the two results are combined together to form the final prediction. A model evaluation is performed using two urban road networks. The results show that the data processing module can effectively improve the data quality, reduce the training time and enhance the model robustness. The LSTM-RNN correctly identifies the time trend and spatial similarity of traffic flow and obtains a more accurate multiple time-step prediction. The proposed framework outperforms other deep learning algorithms and has better accuracy and stability.
机译：提出了一种基于数据驱动方法的城市道路网络短期交通流量预测框架，主要包括两个模块。第一个模块包含一组用于处理交通流数据的算法。经过分析和修复后，将提供不包含异常值的完整数据集以及包含成对的道路段的数据集，这些成对的道路段在趋势方面最为相似。第二个模块着重于多个时间步长的短期预测。在充分了解交通流的周期性和随机性之后，时间序列首先被分解为趋势序列和残差序列。重建两个时间序列后，基于长短期记忆循环神经网络（LSTM-RNN）进行模型训练和预测。最后，将两个结果组合在一起以形成最终预测。使用两个城市道路网络进行模型评估。结果表明，该数据处理模块可以有效提高数据质量，减少训练时间，增强模型的鲁棒性。 LSTM-RNN可以正确识别交通流的时间趋势和空间相似性，并获得更准确的多个时间步长预测。所提出的框架优于其他深度学习算法，并且具有更好的准确性和稳定性。

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