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首页> 外文期刊>IEEE Transactions on Intelligent Transportation Systems >Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning
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Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning

机译：多区域级别乘客需求的短期预测：具有多任务学习的时间卷积神经网络

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摘要

Accurate short-term passenger demand prediction contributes to the coordination of traffic supply and demand. This paper proposes an end-to-end multi-task learning temporal convolutional neural network (MTL-TCNN) to predict the short-term passenger demand in a multi-zone level. Along with a feature selector named spatiotemporal dynamic time warping (ST-DTW) algorithm, this proposed MTL-TCNN is quite qualified for the multi-task prediction problem with the consideration of spatiotemporal correlations. Then, based on the car-calling demand data from Didi Chuxing, Chengdu, China, and taxi demand data from the New York City, the numerical results show that the MTL-TCNN outperforms both classic methods (i.e., historical average (HA), v -support vector machine (v -SVM), and XGBoost) and the state-of-the-art deep learning approaches [e.g., long short-term memory (LSTM) and convolutional LSTM (ConvLSTM)] in both the single task learning (STL) and multi-task learning (MTL) scenarios. In summary, the proposed MTL-TCNN with the ST-DTW algorithm is a promising method for short-term passenger demand prediction in a multi-zone level.
机译：准确的短期乘客需求预测有助于协调交通供需。本文提出了端到端的多任务学习时间卷积神经网络（MTL-TCNN），以预测多区域水平的短期乘客需求。除了名为Spatiotemporal动态时间翘曲（ST-DTW）算法的特征选择器之外，该提出的MTL-TCNN与考虑到时空相关性的多任务预测问题非常合格。然后，根据来自纽约城的Didi Chuxing，Chengdu，China和Taxi的需求数据的呼叫需求数据，数值结果表明，MTL-TCNN优于经典方法（即历史平均（HA）， V -Support向量机（V -SVM）和XGBoost）和最先进的深度学习方法[例如，在单一任务学习中，在单一任务学习中[例如，长期短期内存（LSTM）和卷积LSTM（Convlstm）] （STL）和多任务学习（MTL）方案。总之，具有ST-DTW算法的所提出的MTL-TCNN是用于多区域级别的短期乘客需求预测的有希望的方法。

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