Analyzing the impact of traffic congestion mitigation: From an explainable neural network learning framework to marginal effect analyses

Jianping Sun, Jifu Guo, Xin Wu, Qian Zhu, Danting Wu, Kai Xian, Xuesong Zhou

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number2254
JournalSensors (Switzerland)
Volume19
Issue number10
DOIs
StatePublished - May 2 2019

Keywords

  • Computational graph
  • Congestion mitigation
  • Marginal analyses
  • TensorFlow
  • Traffic demand estimation

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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