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

30 Jul 2020Wei ZengChengqiao LinJuncong LinJincheng JiangJiazhi XiaCagatay TurkayWei 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. However, the widely known modifiable areal unit problem within such aggregation processes can lead to perturbations in the network inputs... (read more)

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