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1. Efficient Data Management for Intelligent Urban Mobility Systems

   Wilbur, Michael; Pugliese, Philip; Laszka, Aron; Dubey, Abhishek (January 2021, Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21))

   null (Ed.)

   Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams. Working with this data presents unique challenges of data management, processing and presentation that is often overlooked by researchers. Therefore, in this work we present an integrated data management and processing framework for intelligent urban mobility systems currently in use by our partner transit agencies. We discuss the available data sources and outline our cloud-centric data management and stream processing architecture built upon open-source publish-subscribe and NoSQL data stores. We then describe our data-integrity monitoring methods. We then present a set of visualization dashboards designed for our transit agency partners. Lastly, we discuss how these tools are currently being used for AI-driven urban mobility applications that use these tools. 

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2. Efficient Data Management for Intelligent Urban Mobility Systems

   Wilbur, Michael; Pugliese, Philip; Laszka, Aron; Dubey, Abhishek (January 2021, Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21))

   Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams. Working with this data presents unique challenges of data management, processing and presentation that is often overlooked by researchers. Therefore, in this work we present an integrated data management and processing framework for intelligent urban mobility systems currently in use by our partner transit agencies. We discuss the available data sources and outline our cloud-centric data management and stream processing architecture built upon open-source publish-subscribe and NoSQL data stores. We then describe our data-integrity monitoring methods. We then present a set of visualization dashboards designed for our transit agency partners. Lastly, we discuss how these tools are currently being used for AI-driven urban mobility applications that use these tools. 

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3. StashCache: A Distributed Caching Federation for the Open Science Grid

   https://doi.org/10.1145/3332186.3332212  

   Weitzel, Derek; Zvada, Marian; Vukotic, Ilija; Gardner, Rob; Bockelman, Brian; Rynge, Mats; Hernandez, Edgar Fajardo; Lin, Brian; Selmeci, Mátyás (January 2019, Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (PEARC ‘19). ACM, New York, NY, USA, Article 58, 7 pages.)

   Data distribution for opportunistic users is challenging as they neither own the computing resources they are using or any nearby storage. Users are motivated to use opportunistic computing to expand their data processing capacity, but they require storage and fast networking to distribute data to that processing. Since it requires significant management overhead, it is rare for resource providers to allow opportunistic access to storage. Additionally, in order to use opportunistic storage at several distributed sites, users assume the responsibility to maintain their data. In this paper we present StashCache, a distributed caching federation that enables opportunistic users to utilize nearby opportunistic storage. StashCache is comprised of four components: data origins, redirectors, caches, and clients. StashCache has been deployed in the Open Science Grid for several years and has been used by many projects. Caches are deployed in geographically distributed locations across the U.S. and Europe. We will present the architecture of StashCache, as well as utilization information of the infrastructure. We will also present performance analysis comparing distributed HTTP Proxies vs StashCache. 

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4. Percolation of temporal hierarchical mobility networks during COVID-19

   https://doi.org/10.1098/rsta.2021.0116  

   He, Haoyu; Deng, Hengfang; Wang, Qi; Gao, Jianxi (January 2022, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   Percolation theory is essential for understanding disease transmission patterns on the temporal mobility networks. However, the traditional approach of the percolation process can be inefficient when analysing a large-scale, dynamic network for an extended period. Not only is it time-consuming but it is also hard to identify the connected components. Recent studies demonstrate that spatial containers restrict mobility behaviour, described by a hierarchical topology of mobility networks. Here, we leverage crowd-sourced, large-scale human mobility data to construct temporal hierarchical networks composed of over 175 000 block groups in the USA. Each daily network contains mobility between block groups within a Metropolitan Statistical Area (MSA), and long-distance travels across the MSAs. We examine percolation on both levels and demonstrate the changes of network metrics and the connected components under the influence of COVID-19. The research reveals the presence of functional subunits even with high thresholds of mobility. Finally, we locate a set of recurrent critical links that divide components resulting in the separation of core MSAs. Our findings provide novel insights into understanding the dynamical community structure of mobility networks during disruptions and could contribute to more effective infectious disease control at multiple scales. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’. 

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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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