Distributed Cloud Data Lakes for Intelligent Transportation Data Integration
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506022Keywords:
Intelligent transportation systems, data integration, cloud computing, big data processing, machine learning, distributed systems, cyber-physical systems, data lakes, distributed cloud storage, metadata management, interoperability, knowledge representation, crosstalkAbstract
Intelligent transportation systems rely on a patchwork of independent data providers and users, hampering holistic applications that improve road safety, increase efficient travel, and reduce carbon emissions. The sheer volume, velocity, and variety of data generated by these systems call for a distributed architecture that allows geographically close data users to share data, collaborate on analytics, and leverage machine learning and statistical modelling at scale for better decision-making. Distributed data lakes, built on elastic cloud-native primitives, allow automated data ingest from multiple providers, storage in purpose-built formats, and batch and real-time analytics. Core design decisions and environmental dependencies inform a target architecture that tackles the classification, ingest, and modeling of vehicular, user-deployed infrastructure, GBFS- and event-driven source data. A proof-of-concept validation using a remote region of Ontario, Canada, proposes specific Cloudflare Workers integration and extends earlier semantic mapping of LTE data to schema evolution.
The volume, velocity, and variety of data generated by intelligent transportation systems (ITS) can support a wide range of applications, including more efficient management of road safety, reduced travel times and vehicle emissions, and increased monetization of supplier data. However, independent data providers and users, such as the City of Toronto's traffic-management system, cannot provide a complete picture. Enabling collaboration on data and analytics is key to delivering truly intelligent system features, yet geographically close data users have traditionally relied on direct links.
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