Estimating the Road Grade Impact on Vehicle Speed and Acceleration on Freeways with a Bayesian Hierarchical Model

This research explores how road grade impacts the operations of light-duty vehicles and heavy-duty express buses on freeways. Second-by-second GPS data of light-duty vehicles from 2011 Atlanta Household and Activity Travel Survey. Three days of operations from 13 express transit buses were also collected via GPS devices. The Bayesian Hierarchical Model (BHM) used in this research employs three variable levels: trace-level, vehicle-level, and fleet-level. The intercept is grouped with the trace-level variables (average speed and acceleration of each trace), that indicate traffic conditions faced by vehicles across each trip trace. The vehicle level parameters represent the effects of specific vehicle performance characteristics on grade (and the hidden effects of driver behavior associated with the vehicle) and display impact heterogeneity across vehicles and drivers. Fleet level parameters capture operations across the entire sample set. First-order autoregressive covariance matrices represent auto-correlation of speed and acceleration within the time series of each trace. Model results show that vehicles tend to operate more conservatively as grades increase, with lower speed and lower acceleration identified. However, it is important to note from the predicted distribution of vehicle-level parameters that significant heterogeneity of road grade impact exists across vehicles. The study results can be used to better model energy consumption and emissions by improving vehicle activity input on freeways segments across various grades. It also provides a reference to microscopic speed and acceleration choices model considering the impact heterogeneity of road grade across vehicle or drivers on hilly freeway.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ80 Standing Committee on Statistical Methods.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Liu, Haobing
    • Rodgers, Michael O
    • Liu, Fang "Cherry"
    • Guensler, Randall
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 8p

Subject/Index Terms

Filing Info

  • Accession Number: 01697897
  • Record Type: Publication
  • Report/Paper Numbers: 19-05901
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:41AM