Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses

Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses

Computational graphs (CGs) have been widely utilized in numerical analysis and deep learning to represent directed forward networks of data flows between operations. This paper aims to develop an explainable learning framework that can fully integrate three major steps of decision support: Synthesis...

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Bibliographic Details
Main Authors: Jianping Sun, Jifu Guo, Xin Wu, Qian Zhu, Danting Wu, Kai Xian, Xuesong Zhou
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Sensors
Subjects:
computational graph
traffic demand estimation
congestion mitigation
marginal analyses
TensorFlow
Online Access:https://www.mdpi.com/1424-8220/19/10/2254

Internet

https://www.mdpi.com/1424-8220/19/10/2254

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