Temporal super-resolution traffic flow forecasting via continuous-time network dynamics
Temporal super-resolution traffic flow forecasting via continuous-time network dynamics
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DOI：
10.1007/s10115-023-01887-6
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发表时间：
2023-06
影响因子：
2.7
通讯作者：
Yinjie Xie;Yun Xiong;Jiawei Zhang;Chao Chen;Yao Zhang;Jie Zhao;Yizhu Jiao;Jinjing Zhao
中科院分区：
文献类型：
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作者：
Yinjie Xie;Yun Xiong;Jiawei Zhang;Chao Chen;Yao Zhang;Jie Zhao;Yizhu Jiao;Jinjing Zhao
交通流预测是智能交通系统的一项重要任务。然而，现有的预测只能在特定的时间步进行，因为数据是离散收集在这些时间步。相反，在真实的世界中，交通流通过连续的方式在真实的时间中演化。因此，一个理想的预测范式应该在任意的时间步长，而不是只在这些特定的时间步长。考虑到预测的时间步长不再受这些时间步长的限制，我们称这种模式为时间超分辨率预测。在本文中，我们将神经常微分方程（神经ODEs）的思想来处理这个问题，建模的交通流量的变化率的城市道路。因此，由于常微分方程的连续性，可以通过对变化率进行定积分来预测任意时间步长的交通流。城市道路通常被看作是一个网络，其变化率可以用连续时间网络动力学来描述，通过对交通流的网络动力学进行参数化来量化其变化率。在此基础上，我们提出了时空连续动力学网络来完成时间超分辨率预测任务。在公共交通流数据集上的大量实验表明，我们的模型可以在时间超分辨率预测上实现高精度，同时确保其在这些特定时间步长的常规实验设置上的性能。
Traffic flow forecasting is a critical task for intelligent transportation systems. However, the existed forecasting can only be conducted at certain time steps, because the data are discretely collected at these time steps. In contrast, traffic flow evolves in real time via a continuous manner in real world. Therefore, an ideal forecasting paradigm should be performed at arbitrary time steps instead of only at these certain time steps. Considering the forecasting time steps will no longer be restricted by these time steps, we call such paradigm as temporal super-resolution forecasting. In this paper, we incorporate the idea of neural ordinary differential equations (neural ODEs) to handle the problem, modeling the change rate of traffic flow on the urban road. Therefore, due to the continuous nature of ordinary differential equations, the traffic flow at arbitrary time steps can be forecasted by performing definite integral for the change rate. The urban road is usually regarded as a network, and the change rate of which can be described by continuous-time network dynamics, we parameterize the network dynamics of the traffic flow to quantify the change rate. On these foundations, we propose spatial-temporal continuous dynamics network to complete the temporal super-resolution forecasting task. Extensive experiments on public traffic flow datasets illustrate that our model can achieve high accuracy on temporal super-resolution forecasting, while ensuring its performance on conventional experimental settings at these certain time steps.