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1. STICAP : Spatio-temporal Interactive Attention for Citywide Crowd Activity Prediction

   https://doi.org/10.1145/3603375  

   Huang, Huiqun; He, Suining; Yang, Xi; Tabatabaie, Mahan (March 2024, ACM Transactions on Spatial Algorithms and Systems)

   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%. 

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2. Towards Dynamic Crowd Mobility Learning and Meta Model Updates for A Smart Connected Campus

   Yang, X.; He, S.; Tabatabaie, M.; Wang, B. (October 2022, International Conference on Embedded Wireless Systems and Networks EWSN)

   In this paper, we propose MetaMobi, a novel spatio-temporal multi-dots connectivity-aware modeling and Meta model update approach for crowd Mobility learning. MetaMobi analyzes real-world Wi-Fi association data collected from our campus wireless infrastructure, with the goal towards enabling a smart connected campus. Specifically, MetaMobi aims at addressing the following two major challenges with existing crowd mobility sensing system designs: (a) how to handle the spatially, temporally, and contextually varying features in large-scale human crowd mobility distributions; and (b) how to adapt to the impacts of such crowd mobility patterns as well as the dynamic changes in crowd sensing infrastructures. To handle the first challenge, we design a novel multi-dots connectivity-aware learning approach, which jointly learns the crowd flow time series of multiple buildings with fusion of spatial graph connectivities and temporal attention mechanisms. Furthermore, to overcome the adaptivity issues due to changes in the crowd sensing infrastructures (e.g., installation of new ac- cess points), we further design a novel meta model update approach with Bernoulli dropout, which mitigates the over- fitting behaviors of the model given few-shot distributions of new crowd mobility datasets. Extensive experimental evaluations based on the real-world campus wireless dataset (including over 76 million Wi-Fi association and disassociation records) demonstrate the accuracy, effectiveness, and adaptivity of MetaMobi in forecasting the campus crowd flows, with 30% higher accuracy compared to the state-of-the-art approaches. 

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3. Toward Heterogeneous Graph-based Imitation Learning for Autonomous Driving Simulation: Interaction Awareness and Hierarchical Explainability

   https://doi.org/10.1145/3708354  

   Tabatabaie, Mahan; He, Suining; Shin, Kang; Wang, Hao (December 2024, ACM Journal on Autonomous Transportation Systems)

   Understanding and learning the actor-to-X interactions (AXIs), such as those between the focal vehicles (actor) and other traffic participants, such as other vehicles and pedestrians, as well as traffic environments like the city or road map, is essential for the development of a decision-making model and the simulation of autonomous driving. Existing practices on imitation learning (IL) for autonomous driving simulation, despite the advances in the model learnability, have not accounted for fusing and differentiating the heterogeneous AXIs in complex road environments. Furthermore, how to further explain the hierarchical structures within the complex AXIs remains largely under-explored. To meet these challenges, we proposeHGIL, an interaction-aware and hierarchically-explainableHeterogeneousGraph-basedImitationLearning approach for autonomous driving simulation. We have designed a novel heterogeneous interaction graph (HIG) to provide local and global representation as well as awareness of the AXIs. Integrating the HIG as the state embeddings, we have designed a hierarchically-explainable generative adversarial imitation learning approach, with local sub-graph and global cross-graph attention, to capture the interaction behaviors and driving decision-making processes. Our data-driven simulation and explanation studies based on the Argoverse v2 dataset (with a total of 40,000 driving scenes) have corroborated the accuracy (e.g., lower displacement errors compared to the state-of-the-art (SOTA) approaches) and explainability ofHGILin learning and capturing the complex AXIs. 

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4. Spatial-temporal modeling of the relationship between surface temperature and air temperature in metropolitan urban systems

   https://doi.org/10.1016/j.uclim.2024.101921  

   Scolio, Madeline; Kremer, Peleg; Zhang, Yimin; Shakya, Kabindra M (May 2024, Urban Climate)

   Research about urban local climate and urban heat island often relies on land surface temperature (LST) data to characterize the distribution of temperature near the surface. Although using remotely sensed data for such work has the advantage of continuous spatial coverage at regular temporal intervals, it is recognized that surface temperature is not an ideal proxy for air temperature (AT). This study's goal is to develop a spatiotemporal model revealing the relationship between LST and AT within the complexities of the urban environment. A mobile weather monitoring unit was used to collect spatially-explicit fine-scale AT data while Landsat 8 and 9 passed overhead collecting LST data. A spatiotemporal model of the relationship between LST and AT in Philadelphia was constructed with this data utilizing basis functions to account for spatial and temporal autocorrelation. The spatiotemporal model results show a strong relationship between LST and AT and indicate that it is possible to predict fine scale AT (120 m) using remotely sensed LST in an urban context (r-squared = 0.99, RMSE = 0.89 ◦C). The spatiotemporal model outperforms models that do not account for spatial and temporal autocorrelation, highlighting the importance of considering these dependencies in temperature modeling. City-wide AT predictions were generated for Philadelphia demonstrating the ability of the model to improve understanding of local urban climate. 

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5. Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction

   https://doi.org/10.1145/3673227  

   Mo, Zhaobin; Xiang, Haotian; Di, Xuan (July 2024, ACM Transactions on Spatial Algorithms and Systems)

   The COVID-19 pandemic has dramatically transformed human mobility patterns. Therefore, human mobility prediction for the “new normal” is crucial to infrastructure redesign, emergency management, and urban planning post the pandemic. This paper aims to predict people’s number of visits to various locations in New York City using COVID and mobility data in the past two years. To quantitatively model the impact of COVID cases on human mobility patterns and predict mobility patterns across the pandemic period, this paper develops a model CCAAT-GCN (Cross- andContext-Attention based Spatial-TemporalGraphConvolutionalNetworks). The proposed model is validated using SafeGraph data in New York City from August 2020 to April 2022. A rich set of baselines are performed to demonstrate the performance of our proposed model. Results demonstrate the superior performance of our proposed method. Also, the attention matrix learned by our model exhibits a strong alignment with the COVID-19 situation and the points of interest within the geographic region. This alignment suggests that the model effectively captures the intricate relationships between COVID-19 case rates and human mobility patterns. The developed model and findings can offer insights into the mobility pattern prediction for future disruptive events and pandemics, so as to assist with emergency preparedness for planners, decision-makers and policymakers. 

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