The use of the current global and regional models for predicting large-scale urban crowd flows often involves trade-offs between computational efficiency and accuracy. Global models are computationally efficient but struggle to fully capture the spatial heterogeneity in crowd dynamics and often lead to unsatisfactory performance. Region-specific models are highly accurate in capturing fine-grained spatial heterogeneity, but their computational costs are high when applied to numerous regions. We took a GeoAI approach to develop a novel spatiotemporal compressed sensing-based prediction framework (STCSP) to address these challenges. This framework employs compressed sensing techniques to identify the shared structures in crowd flow data. STCSP transforms spatiotemporal predictions in a complex geographical space into simplified predictions in an embedding space, which is more efficient than existing models. STCSP combines these simplified predictions, modeling the spatial heterogeneity in detail to increase the accuracy of crowd-flow predictions. We evaluated STCSP on a small-scale benchmark dataset and a large-scale citywide dataset and showed that STCSP outperformed 12 baseline models in accuracy and efficiency in predicting crowd flows.

Urban areas comprise numerous spatial communities due to the frequent and limited range of human movements. Due to the partial spatial stochasticity of human movements, urban spatial communities are fuzzy and spatially heterogeneous. Existing spatial community detection strategies based on deterministic and globally uniform criteria fail to account for these characteristics. Therefore, this study presents a framework for detecting spatial fuzzy communities by transforming spatial fuzzy community detection into trip estimations between spatial units. We developed a probabilistic optimal estimation (ProOE) method to estimate trip volumes between spatial units by adjusting the probability of the membership of each unit in a spatial community. A trip intensity parameter was introduced for each community to adjust the estimated trip volumes. The distance decay effect (DDE) of human movement was then incorporated into the model, further improving the accuracy of community delineation for specific cities. Finally, spatial continuity guidance was incorporated into the solution algorithm, minimizing unnecessary community fragmentation. The experimental results demonstrate that ProOE outperforms existing methods, achieving an average improvement of 31.57% in accuracy while effectively capturing the ambiguity in the interplay between spatial units and communities. This study contributes to a more precise understanding of the spatial structures of cities.

The detection of anomalous paths from floating vehicle trajectories plays an increasingly important role in dynamic path planning because of its ability to identify fraudulent transport behaviors, unexpected traffic accidents, and traffic restriction areas. Existing studies mostly emphasize the significant deviations based on global shape metrics and cannot manage trajectories with missing segments. To address these problems, this study proposes a new sequential pattern mining based method for detecting anomalous paths (SPDAP) in floating vehicle trajectories. First, discrete trajectory points were transformed into consecutive road segment sequences using map matching and essential empirical information. Focusing on these segment sequences, this method constructs a directed graph and calculates the conditional occurrence probabilities of multi-order sub-paths on the graph. On this basis, we designed a sequential pattern mining algorithm by estimating kernel density distributions to adaptively extract multi-order frequent sub-paths. Finally, these anomalous paths can be identified by matching the operations with frequent paths. Comparative experiments on simulated trajectories demonstrated that SPDAP could accurately, adequately, and adaptively detect anomalous paths. Additionally, using real-life taxi trajectories, we conducted semantic analysis by considering travel time and charges to further derive four patterns from the detected anomalous paths. These patterns are expected to support the identification of fraudulent taxi trips and the discovery of candidate driving routes with a preference for saving time or charge. In the future, we will focus on tracing the intrinsic causes of anomalous paths by introducing multisource information and performing real-time anomalous path detection using adaptive time intervals.

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments.

Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization.

Monitoring various types of disasters involves diversified requirements, such as the spectral band, resolution, and timeliness. However, at present, different types of observation platforms are separately operated. This isolated resource organization model is insufficient to meet the requirements of various Earth-observation tasks, especially when disasters occur. As a result, it is necessary to construct an Earth-observation network that contains space-air-ground observation resources and makes unified task planning for the included heterogeneous resources, such that the efficiency of the entire observation system is maximized. In this article, an architecture with two planning phases is proposed for the coordinated planning of heterogeneous Earth-observation resources, in which area targets and four types of space-air-ground observation resources [i.e., satellites, unmanned aerial vehicles (UAVs), airships, and ground monitoring vehicles] are considered. The two-phase approach in this architecture includes an area target decomposition phase and a task allocation phase. In the first phase, an area target hierarchical decomposition (ATHD) method is proposed to decompose the area targets into subtasks. In the second phase, a task conflict heuristic allocation (TCHA) method is proposed to allocate the decomposed subtasks to subplanning centers. Extensive experiments on simulated and realistic scenarios are conducted to verify the effectiveness of the proposed ATHD and TCHA methods. The computational results show that the ATHD method substantially improves the efficiency of the coordinated task planning process. Moreover, compared with traditional task allocation methods, the TCHA method could produce high-quality observation plans for the Earth-observation network, as it brings complementary benefits via the comprehensive usage of heterogeneous space-air-ground resources.