Deep spatiotemporal residual early-late fusion network for city region vehicle emission pollution prediction

Regulation on the urban vehicle emission has great impact on our daily lives and can protect public health. However, there are sparse emission remote sensing stations in city, and vehicle emission data is both spatial and temporal non-stationary, which is influenced by various internal and external factors, such as spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), environments (road network, meteorology, events, traffic flow and POIs). In this paper, we introduce a semi-supervised learning approach with co-training geographical weighted regression model, which aims to construct the historical emission observations with the insufficient stations records. And then we formulate the region emission prediction as a spatiotemporal sequence forecasting problem and propose a deep spatiotemporal residual early-late fusion network based on unique properties of spatiotemporal data, to predict vehicle emissions in each region of the given city. And the residual convolution network is employed to model the temporal properties of region vehicle emissions. Finally, we present experiments with the remote sensing records of Hefei, where the proposed model outperforms the other baselines. This result demonstrates that combining deep spatiotemporal residual early-late fusion network with the semi-supervised geographical weighted regression can predict vehicle emission in each region of city effectively. (C) 2019 Elsevier B.V. All rights reserved.