Learning Traffic Flow Dynamics Using Random Fields

IEEE Access

Deepthi Mary Dilip¹  Dianchao Lin²  Bilal Thonnam Thodi²  Saif Eddin G. Jabari³ 

[1] Department of Civil Engineering, Birla Institute of Technology and Science, Pilani (BITS Pilani), Dubai Campus, Dubai, United Arab Emirates;Department of Civil and Urban Engineering, New York University (NYU) Tandon School of Engineering, Brooklyn, NY, USA;Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates;

关键词: Stochastic traffic dynamics;    conditional random fields;    Markov random fields;    factor graphs;    traffic state estimation;   

DOI  :  10.1109/ACCESS.2019.2941088

来源: DOAJ

【 摘 要 】

This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy.

【 授权许可】

Unknown