Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways. Issue 3 (1st January 2020)

• Record Type: Journal Article
• Authors: Liu, Haobing; Rodgers, Michael O.; Liu, Fang "Cherry"; Guensler, Randall
• Is Part Of: Transportmetrica. Volume 16:Issue 3(2020)
• Page Start: 602
• Page End: 625
• Publication Date: 2020-01-01
• Subjects: Road grade -- speed -- acceleration -- Bayesian hierarchical model -- Markov Chain Monte Carlo -- Gibbs sampler -- Metropolis-Hasting (M-H) algorithm -- heterogeneity -- heteroscedasticity -- autocorrelation; Transportation -- Periodicals; Transportation -- Research -- Periodicals; 388.072
• DOI: 10.1080/23249935.2020.1722280
• Languages: English
• ISSNs: 2324-9935

Abstract

ABSTRACT: This research explores how road grade impacts the operations of light-duty vehicles and heavy-duty express buses on freeways. The Bayesian Hierarchical Model (BHM) used in this research employs three variable levels: trace-level (for individual trip effects), vehicle-level (for individual vehicle effects), and fleet-level (for overall sample effect). The vehicle level parameters represent the effects of specific vehicle performance characteristics and drivers' behaviors on grade (and the hidden effects of driver behavior associated with the vehicle) and display random 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. Significant heterogeneity of road grade impact is observed across vehicles. The study provides a reference to microscopic speed and acceleration choices model considering the impact heterogeneity of road grade across vehicles or drivers on the hilly freeway.