.png){kind=icon}
返回
Learning Traffic Flow Dynamics Using Random Fields
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2941088.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Stochastic traffic dynamics
conditional random fields
Markov random fields
factor graphs
traffic state estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Material transfer in accretionary wedges from analysis of a systematic series of analog experiments
.png){kind=icon}
PREAI
分享收藏Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing混合连接车辆和常规车辆的高速公路交通状态估计: 基于微观仿真的测试
PREAI
分享收藏Probe vehicle-based traffic state estimation method with spacing information and conservation lawOAAI
OAAI分享收藏
查看更多内容