Michael Wilbur
ABSTRACT
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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- Jose Paolo Talusan, Michael Wilbur, Abhishek Dubey, and Keiichi Yasumoto. 2020. On Decentralized Route Planning Using the Road Side Units as Computing Resources. In 2020 IEEE International Conference on Fog Computing (ICFC). IEEE, 1--8.Google ScholarCross Ref
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- Michael Wilbur, Chinmaya Samal, Jose Paolo Talusan, Keiichi Yasumoto, and Abhishek Dubey. 2020. Time-dependent Decentralized Routing using Federated Learning. In 2020 IEEE 23rd International Symposium on Real-Time Distributed Computing (ISORC). IEEE, 56--64.Google ScholarCross Ref
Index Terms
- Efficient Data Management for Intelligent Urban Mobility Systems
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