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

1
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2
Beijing Transport Institute, Beijing 100073, China
3
School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA
4
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(10), 2254; https://doi.org/10.3390/s19102254
Received: 1 March 2019 / Revised: 30 April 2019 / Accepted: 7 May 2019 / Published: 15 May 2019
(This article belongs to the Special Issue Sensors for Transportation Systems)
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PDF [6124 KB, uploaded 16 May 2019]
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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. View Full-Text
Keywords: computational graph; traffic demand estimation; congestion mitigation; marginal analyses; TensorFlow computational graph; traffic demand estimation; congestion mitigation; marginal analyses; TensorFlow
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Sun, J.; Guo, J.; Wu, X.; Zhu, Q.; Wu, D.; Xian, K.; Zhou, X. Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses. Sensors 2019, 19, 2254.

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