No abstract

Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease the traffic pressure and improve passengers’ experience. Traditional taxi demand prediction methods mostly rely on time-series forecasting techniques, which cannot model the nonlinearity embedded in data. Recent studies start to combine the Euclidean spatial features through grid-based methods. By considering the spatial correlations among different regions, we can capture how the temporal events have impacts on those with adjacent links or intersections and improve prediction precision. Some graph-based models are proposed to encode the non-Euclidean correlations as well. However, the temporal periodicity of data is often overlooked, and the study units are usually constructed as oversimplified grids. In this paper, we define places with specific semantic and humanistic experiences as study units, using a fuzzy set method based on adaptive kernel density estimation. Then, we introduce dual temporal gated multi-graph convolution network to predict the future taxi demand. Specifically, multi-graph convolution is used to model spatial correlations with graphs, including the neighborhood, functional similarities and landscape similarities based on street view images. As for the temporal dependencies modeling, we design the dual temporal gated branches to capture information hidden in both previous and periodic observations. Experiments on two real-world datasets show the effectiveness of our model over the baselines.

Abstract. Aircraft target detection in airport remote sensing image by deep learning is a hot topic of current research, however, training deep network model needs a large amount of datasets, and there is no website or the specialized agencies to collect remote sensing images of airport. To solve the problem, a multi-view images generation method for aircraft targets is proposed in this paper. Firstly, the aircraft viewpoint transformation detection imaging model was established, and the coordinate transformation is deduced in detail; secondly, a multi-view image simulation algorithm structure was designed, and the realization of the algorithm is introduced in detail. Experimental results show that the method has good simulation effect on remote sensing images of airport and have high use value.