Bicycle Exposure Models Using Bayesian Updating

R. T. Panik, K. E. Watkins, I. TienConference room

  1. Monitoring bicycle traffic is difficult due to agency limitations, noisy data, and highly variable bike travel patterns.
  2. This work investigates the feasibility of using a Bayesian framework for estimating cycling volumes.
  3. The models use crowd-sourced, segment level data to inform priors and strategic counts for updating.
  4. Results will indicate whether Bayesian approaches might improve modeling when observed data is limited.

Mon 19:49 - 00:00