A Systematic Evaluation of Generative Models on Tabular Transportation Data

Details

• Creators:
  Wang, Chengen ; Cardenas, Alvaro A. ; Comert, Gurcan ; Kantarcioglu, Murat
• Corporate Creators:
  University of Texas at Dallas ; University of California, Santa Cruz ; North Carolina A&T State University
• Subject/TRT Terms:
  • Data collection
  • Transportation
  • Data models
  • Evaluation
  • Data files
  • Statistical analysis
  • Case studies
  • Tabular Data Synthesis
  • Transportation Data
  • Generative Models Evaluation
• Resource Type:
  Research Paper
• Geographical Coverage:
  United States ; New York
• Corporate Publisher:
  University of Texas at Dallas ; University of California, Santa Cruz ; Center for Advanced Transportation Mobility, North Carolina A&T State University
• Abstract:
  The sharing of large-scale transportation data is beneficial for transportation planning and policymaking; however, there are privacy concerns with data sharing, as it can include identifiable personal information, such as individuals’ home locations. To address these concerns, synthetic data generation based on real transportation data offers a promising solution that allows privacy protection while potentially preserving data utility. Although there are various synthetic data generation techniques, they are often not tailored to the unique characteristics of transportation networks. In this paper, we use New York City taxi data as a case study to conduct a systematic evaluation of the performance of widely used tabular data generative models. In addition to traditional metrics such as distribution similarity, coverage, and privacy preservation, we propose a novel graph-based metric tailored specifically for transportation data.
• Content Notes:
  Copyright The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025 ; X. Wu et al. (Eds.): PAKDD 2025, LNAI 15876, pp. 106–118, 2025. https://doi.org/10.1007/978-981-96-8298-0_9
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86967/dot_86967_DS1.pdf
• File Type:
  [PDF - 281.02 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:387290da193b3396ef4ba5fb418d5c8177ebd5b8a7331781e487aa59bbd71e11cb8b29d4874cce1cd956f15104d283e07c3c9aa98b5029aebb98ca9da923635a