Enabling Analysis and Visualization of Transportation Big Data
Transportation studies generate massive amounts of data that are difficult to store, process, query and visualize quickly and easily. Overcoming these challenges are an essential aspect of making the collected data useful to both the original study and other research that could build on the results. We explore the impact of database implementation, specifically IoTDB, on these aspects of data management with respect to transportation on existing datasets.
Authors
- Daniel B. Work (ORCID: https://orcid.org/0000-0003-0565-2158)
- Alexandre M. Bayen (ORCID: https://orcid.org/0000-0002-6697-222X)
- Jonathan Sprinkle (ORCID: https://orcid.org/0000-0003-4176-1212)
- Xia Wang (ORCID: https://orcid.org/0000-0003-0674-3692)
- Stephen A. Rees (ORCID: https://orcid.org/0000-0003-0949-7351)
- Jonathan Lee (ORCID: https://orcid.org/0000-0003-3932-761X)
- Matt Bunting (ORCID: https://orcid.org/0009-0008-8726-6413)
- Benedetto Piccoli (ORCID: https://orcid.org/0000-0002-6000-5394)
- Maria Laura Delle Monache (ORCID: https://orcid.org/0000-0003-1355-8153)
Institutions
- Rutgers, The State University of New Jersey (US)
- Vanderbilt University (US)
- University of California, Berkeley (US)
Publication Details
- Published
- 2025-05-03
- DOI
- https://doi.org/10.1145/3722573.3727833
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00