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A Multitask Learning Neural Network for Short-Term Traffic Speed Prediction and Confidence Estimation

机译：用于短期交通速度预测和置信度估计的多任务学习神经网络

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To improve predictive accuracy on short-term traffic speed, we proposed a multitask learning neural network (MLNN). MLNN carries out the speed prediction task for three short-terms by the combination of convolution neural network (CNN) and gated recurrent units' network (GRU), and accomplishes the confidence estimation task on predicted speed with the confidence network. A multitask loss function with weighted sub loss terms for multitask learning is employed. In the experiment, our method was tested on the data set of Shanghai Expressway at 2014. Conventional methods such as auto-regressive integrated moving average (ARIMA) and Gaussian maximum likelihood (GML), and time series models, recurrent neural network (RNN), GRU and long short-term memory (LSTM), were also used to compare. The results show that MLNN with square loss obtained the smallest mean squared error (MSE) on most cases. For four road types, MLNN obtained the overall smallest mean absolute percentage error (MAPE) on these cases. We also proved that as compared to single-term prediction, multitask learning outperformed 12.4% in MSE and 9.91% in MAPE for 10-min and 15-min prediction. To improve the forecast on low speed, MAP-loss term is additionally used in multitask loss function. It efficiently improved the predictive accuracy on low speed. The confidence estimation network gave a 89.93% estimation accuracy on the predicted speed, efficiently avoiding the inaccurate speed prediction.
机译：为了提高短期交通速度的预测准确性，我们提出了一个多任务学习神经网络（MLNN）。 MLNN通过卷积神经网络（CNN）和门控复发单元的网络（GRU）的组合来执行三个短期的速度预测任务，并完成与置信网络预测速度的置信度估计任务。采用具有加权子丢失学习的多任务丢失功能。在实验中，我们的方法在2014年上海高速公路的数据集上进行了测试。常规方法，如自动回归综合移动平均（ARIMA）和高斯最大似然（GML），以及时间序列模型，经常性神经网络（RNN） ，GRU和长短短期记忆（LSTM）也用于比较。结果表明，在大多数情况下，具有方形损耗的MLNN获得了最小的平均平方误差（MSE）。对于四种道路类型，MLNN在这些情况下获得了整体最小的平均绝对百分比误差（MAPE）。我们还证明，与单学期预测相比，MSE的多任务学习表现出12.4％，在MAPE中为10分钟和15分钟的预测中的9.91％。为了改善低速预测，映射损耗项还用于多任务丢失函数。它有效地提高了低速的预测精度。置信度估计网络对预测速度的估计精度有效，有效地避免了不准确的速度预测。

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