Comparing traffic state estimators for mixed human and automated traffic flows. (May 2017)

• Record Type: Journal Article
• Authors: Wang, Ren; Li, Yanning; Work, Daniel B.
• Is Part Of: Transportation research. Volume 78(2017)
• Page Start: 95
• Page End: 110
• Publication Date: 2017-05
• Subjects: Traffic state estimation -- Automated vehicles -- Second order traffic flow model; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.02.011
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Established the applicability of the 2 × 2 model for mixed human and automated traffic. The performance of the 2 × 2 and scalar models are compared using particle filtering. Micro simulations show higher performance of 2 × 2 model estimator at high variability. Abstract: This article addresses the problem of modeling and estimating traffic streams with mixed human operated and automated vehicles. A connection between the generalized Aw Rascle Zhang model and two class traffic flow motivates the choice to model mixed traffic streams with a second order traffic flow model. The traffic state is estimated via a fully nonlinear particle filtering approach, and results are compared to estimates obtained from a particle filter applied to a scalar conservation law. Numerical studies are conducted using the Aimsun micro simulation software to generate the true state to be estimated. The experiments indicate that when the penetration rate of automated vehicles in the traffic stream is variable, the second order model based estimator offers improved accuracy compared to a scalar modeling abstraction. When the variability of the penetration rate decreases, the first order model based filters offer similar performance.