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Hybrid LSTM Neural Network for Short-Term Traffic Flow Prediction

机译：混合LSTM神经网络用于短期交通流量预测

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摘要

The existing short-term traffic flow prediction models fail to provide precise prediction results and consider the impact of different traffic conditions on the prediction results in an actual traffic network. To solve these problems, a hybrid Long Short–Term Memory (LSTM) neural network is proposed, based on the LSTM model. Then, the structure and parameters of the hybrid LSTM neural network are optimized experimentally for different traffic conditions, and the final model is compared with the other typical models. It is found that the prediction error of the hybrid LSTM model is obviously less than those of the other models, but the running time of the hybrid LSTM model is only slightly longer than that of the LSTM model. Based on the hybrid LSTM model, the vehicle flows of each road section and intersection in the actual traffic network are further predicted. The results show that the maximum relative error between the actual and predictive vehicle flows of each road section is 1.03%, and the maximum relative error between the actual and predictive vehicle flows of each road intersection is 1.18%. Hence, the hybrid LSTM model is closer to the accuracy and real-time requirements of short-term traffic flow prediction, and suitable for different traffic conditions in the actual traffic network.
机译：现有的短期交通流量预测模型无法提供精确的预测结果，而无法考虑实际交通网络中不同交通状况对预测结果的影响。为了解决这些问题，基于LSTM模型，提出了一种混合的长短期记忆（LSTM）神经网络。然后，针对不同的交通状况对混合LSTM神经网络的结构和参数进行了实验优化，并将最终模型与其他典型模型进行了比较。发现混合LSTM模型的预测误差明显小于其他模型，但是混合LSTM模型的运行时间仅比LSTM模型的运行时间稍长。基于混合LSTM模型，可以进一步预测实际交通网络中每个路段和交叉路口的车辆流量。结果表明，每个路段的实际和预测车辆流量之间的最大相对误差为1.03％，每个路口的实际和预测车辆流量之间的最大相对误差为1.18％。因此，混合LSTM模型更接近于短期交通流量预测的准确性和实时性要求，并且适合于实际交通网络中的不同交通状况。

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