Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost; 2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method; 2) Cross-city travel cost prediction through disentangled adversarial training; 3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability.

With the development of the polarimetric statistical measures, change detection algorithms have been widely applied in the flood disaster detection. The representative methods utilize the Wishart distance to generate the difference map between the pre- and the post-disaster data. Furthermore, the difference map is applied into the threshold segmentation to generate the binary result. However, the existing methods rarely consider the impact of rainfall on the polarimetric distance, which causes abnormal polarimetric differences and incorrect detection results. Based on the fact of different sensitivities of the co- and the cross-polarization intensity for the rainfall, this paper uses the cross ratio of two polarization intensities to eliminate the impacts of the rainfall events on the polarimetric distances. Then the Markov Random Field (MRF) model is utilized to detect the flood disaster with the initialization of OTSU threshold segmentation. Two Sentinel-1 SAR data in the Poyang Lake Basin is used to assess the effectiveness of our method. The results have demonstrated the superiority of our method over the conventional methods, especially in the rainy areas.

“Virtual statistics,” as we define them, are estimators of performance measures that are conditional on the occurrence of an event; virtual waiting time of a customer arriving to a queue at time [Formula: see text] is one example of virtual performance. In this paper, we describe a [Formula: see text]-nearest-neighbor method for estimating virtual performance postsimulation from the retained sample paths, examining both its small-sample and asymptotic properties and providing two approaches for measuring the error of the [Formula: see text]-nearest-neighbor estimator. We implement leave-one-replication-out cross-validation for tuning a single parameter [Formula: see text] to use for any time (or times) of interest and evaluate the prediction performance of the [Formula: see text]-nearest-neighbor estimator via controlled studies. As a by-product, this paper motivates a different way of thinking about how to process the output from dynamic, discrete-event simulation.