.png){kind=icon}
返回
Quantify the Road Link Performance and Capacity Using Deep Learning Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TITS.2022.3153397.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The link performance and capacity are important quantitative features in road link performance assessment, and they play vital roles in many important transportation tasks, e.g., traffic assignment and dynamic routing. However, it remains a challenging task to quantify them, particularly in a dynamic traffic scenario requiring an accurate, fast, and dynamic output. This study proposes a tailored deep learning framework for the addressed problem, which combines important transport domain knowledge reflected by the Bureau of Public Road (BPR) link performance function. In specifics, the calibration of link performance function and the estimation of link travel time are combined in the proposed framework and realized by two neural network modules. Numerical experiments demonstrate the capability of the proposed framework to capture complex relationships between dynamic link capacity and various factors and show its value in estimating link travel time.
Keyword:
Business process re-engineering
Roads
Estimation
Neural networks
Transportation
Calibration
Deep learning
Link performance function
link capacity
deep learning
BPR function
macroscopic and microscopic traffic modeling
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
机构
引用论文
.png){kind=icon}
Estimation of urban network capacity with second-best constraints for multimodal transport systems
PREAI
分享收藏A hybrid deep learning based traffic flow prediction method and its understanding基于混合深度学习的交通流预测方法及其理解
PREAI
分享收藏
查看更多内容