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
Published in:Sensors (Basel, Switzerland) Switzerland), 2019-05, Vol.19 (10), p.2254
Main Authors: Sun, Jianping, Guo, Jifu, Wu, Xin, Zhu, Qian, Wu, Danting, Xian, Kai, Zhou, Xuesong
Format: Article
Language:English
Subjects:
Artificial intelligence
Big Data
Cellular telephones
Clinical decision making
computational graph
congestion mitigation
Decision making
Deep learning
Estimates
Impact analysis
Informatics
Machine learning
Mapping
marginal analyses
Native North Americans
Numerical analysis
Researchers
Route choice
TensorFlow
Traffic assignment
Traffic congestion
traffic demand estimation
Traffic surveys
Travel
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Summary: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 of diverse traffic data, multilayered traffic demand estimation, and marginal effect analyses for transport policies. Following the big data-driven transportation computational graph (BTCG) framework, which is an emerging framework for explainable neural networks, we map different external traffic measurements collected from household survey data, mobile phone data, floating car data, and sensor networks to multilayered demand variables in a CG. Furthermore, we extend the CG-based framework by mapping different congestion mitigation strategies to CG layers individually or in combination, allowing the marginal effects and potential migration magnitudes of the strategies to be reliably quantified. Using the TensorFlow architecture, we evaluate our framework on the Sioux Falls network and present a large-scale case study based on a subnetwork of Beijing using a data set from the metropolitan planning organization.
ISSN:1424-8220
1424-8220
DOI:10.3390/s19102254