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• W3042714573 abstract "Modeling stochastic traffic behaviors at the microscopic level, such as car-following and lane-changing, is a crucial task to understand the interactions between individual vehicles in traffic streams. Leveraging a recently developed theory named physics regularized Gaussian process (PRGP), this study presents a stochastic microscopic traffic model that can capture the randomness and measure errors in the real world. Physical knowledge from classical car-following models is converted as physics regularizers, in the form of shadow Gaussian process (GP), of a multivariate PRGP for improving the modeling accuracy. More specifically, a Bayesian inference algorithm is developed to estimate the mean and kernel of GPs, and an enhanced latent force model is formulated to encode physical knowledge into stochastic processes. Also, based on the posterior regularization inference framework, an efficient stochastic optimization algorithm is developed to maximize the evidence lower-bound of the system likelihood. To evaluate the performance of the proposed models, this study conducts empirical studies on real-world vehicle trajectories from the NGSIM dataset. Since one unique feature of the proposed framework is the capability of capturing both car-following and lane-changing behaviors with one single model, numerical tests are carried out with two separated datasets, one contains lane-changing maneuvers and the other doesn't. The results show the proposed method outperforms the previous influential methods in estimation precision." @default.
• W3042714573 created "2020-07-23" @default.
• W3042714573 creator A5001784237 @default.
• W3042714573 creator A5003080721 @default.
• W3042714573 creator A5084016605 @default.
• W3042714573 date "2020-07-17" @default.
• W3042714573 modified "2023-09-27" @default.
• W3042714573 title "Modeling Stochastic Microscopic Traffic Behaviors: a Physics Regularized Gaussian Process Approach." @default.
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• W3042714573 hasPublicationYear "2020" @default.
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