Traffic flow prediction using Deep Sedenion Networks

Alabi Bojesomo, Panos Liatsis, Hasan Al Marzouqi

Traffic4cast2020 is the second year of NeurIPS competition where participants are to predict traffic parameters (speed and volume) for 3 different cities. The information provided includes multi-channeled multiple time steps inputs and predicted output. Multiple output time steps were viewed as a multi-task segmentation in this work, forming the basis for applying hypercomplex number based UNet structure. The presence of 12 spatio-temporal multi-channel dynamic inputs and single static input, while sedenion numbers are 16-dimensional (16-ions) forms the basis of using sedenion hypercomplex number. We use 12 of the 15 sedenion imaginary parts for the dynamic inputs and the real part is used for the static input. The sedenion network enables the solution of this challenge by using multi-task learning, a situation of the traffic prediction with different time steps to compute.

Knowledge Graph



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