The formulation and estimation of a spatial skew-normal generalized ordered-response model.

Details

• Creators:
  Bhat, Chandra R. ; Astroza, Sebastian ; Hamdi, Amin S.
• Corporate Creators:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Skewed distributions
  • Econometrics
  • Spatial analysis
  • Bicycle travel
  • Bicycle lanes
  • Generalized ordered-response model
  • social interactions
  • composite marginal likelihood
  • spatial econometrics
  • bicycling frequency
• Publication/ Report Number:
  D-STOP/2016/117
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Washington
• Corporate Publisher:
  University of Texas at Austin. Data-Supported Transportation Operations & Planning Center (D-STOP)
• Abstract:
  This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an ; associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal ; distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is ; estimated using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) inference ; approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound ; household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our ; results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an ; individual’s immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and ; young individuals (18-34 years of age) in particular “warm up” to bicycling as more investment is made in ; bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure ; enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social ; dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest ; that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of ; bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments ; in bicycling infrastructure projects.
  More +
• Format:
  PDF
• Funding:
  DTRT13-G-UTC58
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31771/dot_31771_DS1.pdf
• File Type:
  [PDF - 599.26 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:c476973d27016ee4f6ff3d34ea98cd9f5077e55c355a1d85a30a2c87fa8309c3