论文标题
使用深层镇静网络的交通流量预测
Traffic flow prediction using Deep Sedenion Networks
论文作者
论文摘要
在本文中，我们将解决方案介绍给Traffic4cast2020流量预测挑战。在这场比赛中，参与者将在三个不同的城市中预测未来的交通参数（速度和量）：柏林，伊斯坦布尔和莫斯科。所提供的信息包括九个通道，其中前八个代表四个不同方向的速度和音量（NE，NW，SE和SW），而最后一个渠道则用于指示存在交通事故的存在。预期的输出应在未来的六个定时间隔（5、10、15、30、45和60分钟）中具有输入的前8个频道，而过去5分钟的交通数据的持续时间为5分钟，以输入为输入。我们使用新型Sedenion U-NET神经网络解决了问题。 Sedenion网络提供了有效编码相关的多模式数据集的手段。我们将15个镇静零件中的12个用于动态输入，而实际的静态组件用于静态输入。网络的镇静输出用于表示多模式的流量预测。拟议的系统实现了1.33E-3的验证MSE，测试MSE为1.31E-3。
In this paper, we present our solution to the Traffic4cast2020 traffic prediction challenge. In this competition, participants are to predict future traffic parameters (speed and volume) in three different cities: Berlin, Istanbul and Moscow. The information provided includes nine channels where the first eight represent the speed and volume for four different direction of traffic (NE, NW, SE and SW), while the last channel is used to indicate presence of traffic incidents. The expected output should have the first 8 channels of the input at six future timing intervals (5, 10, 15, 30, 45, and 60min), while a one hour duration of past traffic data, in 5mins intervals, are provided as input. We solve the problem using a novel sedenion U-Net neural network. Sedenion networks provide the means for efficient encoding of correlated multimodal datasets. We use 12 of the 15 sedenion imaginary parts for the dynamic inputs and the real sedenion component is used for the static input. The sedenion output of the network is used to represent the multimodal traffic predictions. Proposed system achieved a validation MSE of 1.33e-3 and a test MSE of 1.31e-3.
