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Accurate prediction of citywide crowd activity levels (CALs),i.e., the numbers of participants of citywide crowd activities under different venue categories at certain time and locations, is essential for the city management, the personal service applications, and the entrepreneurs in commercial strategic planning. Existing studies have not thoroughly taken into account the complex spatial and temporal interactions among different categories of CALs and their extreme occurrences, leading to lowered adaptivity and accuracy of their models. To address above concerns, we have proposedIE-CALP, a novel spatio-temporalInteractive attention-based andExtreme-aware model forCrowdActivityLevelPrediction. The tasks ofIE-CALPconsist of(a)forecasting the spatial distributions of various CALs at different city regions (spatial CALs), and(b)predicting the number of participants per category of the CALs (categorical CALs). To realize above, we have designed a novel spatial CAL-POI interaction-attentive learning component inIE-CALPto model the spatial interactions across different CAL categories, as well as those among the spatial urban regions and CALs. In addition,IE-CALPincorporate the multi-level trends (e.g., daily and weekly levels of temporal granularity) of CALs through a multi-level temporal feature learning component. Furthermore, to enhance the model adaptivity to extreme CALs (e.g., during extreme urban events or weather conditions), we further take into account theextreme value theoryand model the impacts of historical CALs upon the occurrences of extreme CALs. Extensive experiments upon a total of 738,715 CAL records and 246,660 POIs in New York City (NYC), Los Angeles (LA), and Tokyo have further validated the accuracy, adaptivity, and effectiveness ofIE-CALP’s interaction-attentive and extreme-aware CAL predictions. 

Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and theirinteractivedependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we proposeSTICAP, a citywide spatio-temporal interactive crowd activity prediction approach. In particular,STICAPtakes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then, three parallelResidual Spatial Attention Networks(RSAN) in theSpatial Attention Componentexploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by theTemporal Attention Componentforinteractive CAP. Along with other external factors such as weather conditions and holidays,STICAPadaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-world crowd activity datasets have demonstrated that our proposedSTICAPoutperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%.