Data Architectures for AI-Ready Interoperable Public Transportation Ecosystems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916067874439168 |
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| author | Da Silva, Diego de Camargo, Raphael Y. Morais, Mayuri A. Shalaby, Amer |
| author_facet | Da Silva, Diego de Camargo, Raphael Y. Morais, Mayuri A. Shalaby, Amer |
| contents | Public transportation (PT) agencies generate vast amounts of heterogeneous data from automatic fare collection (AFC), automatic passenger counting (APC), vehicle location (AVL/CAD), schedule and real-time feeds (GTFS/GTFS-RT), and proprietary platforms. These datasets offer unprecedented opportunities for data-driven planning, operations, and passenger services, but their potential is constrained by fragmentation, inconsistent update frequencies, and the lack of reproducible, interoperable pipelines. While contemporary data platform patterns and architectural styles from enterprise computing address analogous challenges in other sectors, their adaptation to the PT domain remains mostly underexplored. Transit systems present unique conditions, including the convergence of Information Technology (IT) and Operational Technology (OT), long asset lifecycles, rigorous security requirements, multi-agency coordination requirements, and the need to operate on live systems that preclude controlled experimentation. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2606_00057 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Architectures for AI-Ready Interoperable Public Transportation Ecosystems Da Silva, Diego de Camargo, Raphael Y. Morais, Mayuri A. Shalaby, Amer Emerging Technologies Computational Engineering, Finance, and Science Software Engineering Systems and Control Public transportation (PT) agencies generate vast amounts of heterogeneous data from automatic fare collection (AFC), automatic passenger counting (APC), vehicle location (AVL/CAD), schedule and real-time feeds (GTFS/GTFS-RT), and proprietary platforms. These datasets offer unprecedented opportunities for data-driven planning, operations, and passenger services, but their potential is constrained by fragmentation, inconsistent update frequencies, and the lack of reproducible, interoperable pipelines. While contemporary data platform patterns and architectural styles from enterprise computing address analogous challenges in other sectors, their adaptation to the PT domain remains mostly underexplored. Transit systems present unique conditions, including the convergence of Information Technology (IT) and Operational Technology (OT), long asset lifecycles, rigorous security requirements, multi-agency coordination requirements, and the need to operate on live systems that preclude controlled experimentation. |
| title | Data Architectures for AI-Ready Interoperable Public Transportation Ecosystems |
| topic | Emerging Technologies Computational Engineering, Finance, and Science Software Engineering Systems and Control |
| url | https://arxiv.org/abs/2606.00057 |