ABSTRACT Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time, and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time-series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time-series forecasting; however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural networks including simple RNN units, GRU, and LSTM neural network to predict short-term traffic flow. The dataset from TAP30 Corporation is used for building the models and comparing RNNs with several well-known models, such as DEMA, LASSO, and XGBoost. The results show that all three types of RNNs outperform the others; however, more simple RNNs such as simple recurrent units and GRU perform work better than LSTM in terms of accuracy and training time.

中文翻译：

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使用循环神经网络对在线叫车服务的短期需求预测

摘要 短期交通流量预测是智能交通系统中的关键问题之一，是智慧城市的重要组成部分。准确的预测可以使驾驶员和乘客在出行路线、出发时间和出行起点选择等方面做出更好的决策，有助于交通管理。基于时间序列预测和机器学习的多种模型和算法被应用于这个问题，并取得了可接受的结果。最近，足够的数据和计算能力的可用性促使我们通过深度学习方法提高预测准确性。循环神经网络已成为时间序列预测最流行的方法之一；然而，由于这些网络的多样性，哪种类型最适合这项任务的问题仍未解决。在本文中，我们使用了三种循环神经网络，包括简单的 RNN 单元、GRU 和 LSTM 神经网络来预测短期交通流量。来自 TAP30 Corporation 的数据集用于构建模型并将 RNN 与多个知名模型（如 DEMA、LASSO 和 XGBoost）进行比较。结果表明，所有三种类型的 RNN 都优于其他类型；然而，更简单的 RNN，例如简单的循环单元和 GRU，在准确性和训练时间方面比 LSTM 执行得更好。来自 TAP30 Corporation 的数据集用于构建模型并将 RNN 与多个知名模型（如 DEMA、LASSO 和 XGBoost）进行比较。结果表明，所有三种类型的 RNN 都优于其他类型；然而，更简单的 RNN，例如简单的循环单元和 GRU，在准确性和训练时间方面比 LSTM 执行得更好。来自 TAP30 Corporation 的数据集用于构建模型并将 RNN 与多个知名模型（如 DEMA、LASSO 和 XGBoost）进行比较。结果表明，所有三种类型的 RNN 都优于其他类型；然而，更简单的 RNN，例如简单的循环单元和 GRU，在准确性和训练时间方面比 LSTM 执行得更好。