Abstract Accurate demand forecasting for online ride-hailing contributes to balancing traffic supply and demand, improving the service level of platforms. In contrast previous studies, which have primarily focused on inflow or outflow demands each zone, this study proposes a Conditional Generative Adversarial Network with Wasserstein divergence objective (CWGAN-div) predict origin-destination (...

Spatiotemporal prediction of crime is crucial for public safety and smart cities operation. As incidents are distributed sparsely across space time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in density. This paper proposes the use deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related based on non-emergency service req...

BACKGROUND Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies. OBJECTIVES We studied three important features of the PM2.5 component monitoring data to determine whether it would be ap...

conclusions zebris movement analysis system is a highly reliable instrument for the measurement of gait parameters at different speeds in healthy athletes and those after acl reconstruction surgery. this implies its use in the assessment and treatment process of gait deficits in such a clinically important population. background reconstruction surgery is one of the treatment methods after an an...

We describe a hierarchical appearance-based method for learning, recognizing, and predicting arbitrary spatiotemporal sequences of images. The method, which implements a robust hierarchical form of the Kalman filter derived from the Minimum Description Length (MDL) principle, includes as a special case several well-known object encoding techniques including eigenspace methods for static recogni...

Abstract Spatiotemporal prediction is widely used in the fields of neuroscience, climate, and transportation, traffic speed one typical research areas. Since networks are irregular grid structures with complex nonlinear spatiotemporal dependencies between nodes, traditional single-feature methods difficult to adapt road conditions. In order improve accuracy speed, this paper proposes a method c...

Objective We seek to integrate multiple streams of geo-coded information with the aim to improve public health surveillance accuracy and efficiency. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution can help early prediction of human incidence. To this end, we develop joint modeling approaches to evaluate the contribution of vector or rese...

We identify wave fronts that have passed through atmospheric turbulence as fractal surfaces from the Fractional Brownian motion family. The fractal character can be ascribed to both the spatial and the temporal behavior. The simulation of such wave fronts can be performed with fractal algorithms such as the Successive Random Additions algorithm. An important benefit is that wave fronts can be p...