Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways

Haobing Liu; Michael O. Rodgers; Fang “Cherry” Liu; Randall Guensler

doi:10.1080/23249935.2020.1722280

Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways

Haobing Liu, Michael O. Rodgers, Fang “Cherry” Liu, Randall Guensler

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

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.

Original language	English
Pages (from-to)	602-625
Number of pages	24
Journal	Transportmetrica A: Transport Science
Volume	16
Issue number	3
DOIs	https://doi.org/10.1080/23249935.2020.1722280
State	Published - Jan 1 2020
Externally published	Yes

Funding

This work was sponsored by the USDOT's University Transportation Center program via the National Center for Sustainable Transportation. We would also like to thank Dr. Mehmet (Memo) Belgin in Georgia Tech's PACE (The Partnership for an Advanced Computing Environment) Center for his distributed computing technical support. This work was sponsored by the USDOT?s University Transportation Center program via the National Center for Sustainable Transportation. This work was sponsored by the USDOT's University Transportation Center program via the National Center for Sustainable Transportation. We would also like to thank Dr. Mehmet (Memo) Belgin in Georgia Tech's PACE (The Partnership for an Advanced Computing Environment) Center for his distributed computing technical support.

Funders	Funder number
National Center for Sustainable Transportation
USDOT?s University Transportation Center

Keywords

acceleration
autocorrelation
Bayesian hierarchical model
Gibbs sampler
heterogeneity
heteroscedasticity
Markov Chain Monte Carlo
Metropolis-Hasting (M-H) algorithm
Road grade
speed

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10.1080/23249935.2020.1722280

Cite this

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title = "Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways",

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{\textquoteright} 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.",

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doi = "10.1080/23249935.2020.1722280",

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KW - acceleration

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KW - Markov Chain Monte Carlo

KW - Metropolis-Hasting (M-H) algorithm

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Bayesian approach in estimating the road grade impact on vehicle speed and acceleration on freeways

Abstract

Funding

Keywords

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