Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction

no code implementations • 8 Jan 2021 • Wei Zeng, Chengqiao Lin, Kang Liu, Juncong Lin, Anthony K. H. Tung

Furthermore, to better fit with convolutions, we suggest to first aggregate traffic flows according to pre-conceived regions or self-organized regions based on traffic flows, then dispose to sequentially organized raster images for network input.

Traffic Prediction

Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics

no code implementations • 30 Jul 2020 • Wei Zeng, Chengqiao Lin, Juncong Lin, Jincheng Jiang, Jiazhi Xia, Cagatay Turkay, Wei Chen

Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks.

Traffic Prediction