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

机译：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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摘要

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，我们提出了一种多任务学习神经网络（MLNN）。 MLNN通过卷积神经网络（CNN）和门控递归单元网络（GRU）的组合来执行三个短期的速度预测任务，并用置信度网络完成预测速度的置信度估计任务。采用了具有加权子损失项的多任务损失函数，用于多任务学习。在实验中，我们的方法在2014年的上海高速公路数据集上进行了测试。常规方法包括自回归综合移动平均（ARIMA）和高斯最大似然（GML），以及时间序列模型，递归神经网络（RNN） ，GRU和长短期记忆（LSTM）也用于比较。结果表明，在大多数情况下，具有平方损失的MLNN获得最小的均方误差（MSE）。对于这四种情况，MLNN获得了总体最小平均绝对百分比误差（MAPE）。我们还证明，与单项预测相比，在10分钟和15分钟的预测中，多任务学习在MSE和MAPE方面的表现均优于12.4％和9.91％。为了提高对低速的预测，在多任务丢失功能中还额外使用了MAP丢失项。它有效地提高了低速时的预测准确性。置信度估计网络对预测速度给出了89.93％的估计准确度，有效地避免了速度预测的不准确。

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