Spatial-Temporal Dynamics of Daily and Per Capita VMT in California: A County-Level Analysis (2019–2023)

Details

• Creators:
  Lao, Yong ; Yang, Bo
• Corporate Creators:
  San Jose State University. College of Business. Mineta Transportation Institute ; California State University, Monterey Bay
• Corporate Contributors:
  State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Cluster Analysis Data Analysis Driving Geographic Information Systems K-means Clustering Needs Assessment Predictive Models Regression Analysis Spatial Analysis Transportation Planning Vehicle Miles Of Travel

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• Publication/ Report Number:
  26-07
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  San Jose State University. College of Business. Mineta Transportation Institute
• Abstract:
  Vehicle miles traveled (VMT) is a fundamental metric for assessing mobility trends and infrastructure needs. This study examines the spatial-temporal dynamics of daily VMT (DVMT) and per capita DVMT across California counties from 2019 to 2023, covering the pre-, mid-, and post-pandemic periods via GIS mapping and k-means clustering. To identify determinants of per capita DVMT, we compared traditional linear regression approaches (OLS, Ridge, LASSO, Elastic Net) with ensemble tree-based models. Specific results include: the ensemble models estimated using 2019–2022 data delivered substantially higher accuracy, achieving R² values exceeding 0.98; meanwhile, out-of-sample performance on 2023 data remained robust (R² ≈ 0.82for Random Forest; R² ≈ 0.91 for Gradient Boosting), indicating strong model generalizability. Feature importance analysis identifies housing density, population density, and public transit mode share as the primary drivers of per capita DVMT. These findings underscore the utility of spatial analysis and advanced nonlinear modeling for regional transportation planning.
• Format:
  PDF
• Funding:
  SB1-SJAUX_2023-26
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:952f1efeb9d55c2398d999bd77f212331b9017278323b35426803b996375be7404078b5c3103888bcb2350bd2b2adf2f7e4a7dbc645447774734e62eec8fefda
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89985/dot_89985_DS1.pdf
• File Type:
  [PDF - 13.08 MB ]