Bicycle Exposure Models Using Bayesian Updating
R. T. Panik, K. E. Watkins, I. TienConference room
- Monitoring bicycle traffic is difficult due to agency limitations, noisy data, and highly variable bike travel patterns.
- This work investigates the feasibility of using a Bayesian framework for estimating cycling volumes.
- The models use crowd-sourced, segment level data to inform priors and strategic counts for updating.
- Results will indicate whether Bayesian approaches might improve modeling when observed data is limited.
Wed 23:45 - 00:00
