{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T12:42:20Z","timestamp":1775824940873,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T00:00:00Z","timestamp":1557878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing International Cooperation Base for Science and Technology on Urban Transport and Beijing Key Laboratory of Urban Traffic Operation Simulation and Decision Support","award":["BZ0012"],"award-info":[{"award-number":["BZ0012"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61731004"],"award-info":[{"award-number":["61731004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation\u2013United States","award":["CMMI 1538105"],"award-info":[{"award-number":["CMMI 1538105"]}]},{"name":"National Science Foundation\u2013United States :","award":["CMMI 1663657"],"award-info":[{"award-number":["CMMI 1663657"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19102254","type":"journal-article","created":{"date-parts":[[2019,5,15]],"date-time":"2019-05-15T11:37:40Z","timestamp":1557920260000},"page":"2254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Analyzing the Impact of Traffic Congestion Mitigation: From an Explainable Neural Network Learning Framework to Marginal Effect Analyses"],"prefix":"10.3390","volume":"19","author":[{"given":"Jianping","family":"Sun","sequence":"first","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Transport Institute, Beijing 100073, China"}]},{"given":"Jifu","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"},{"name":"Beijing Transport Institute, Beijing 100073, China"}]},{"given":"Xin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA"}]},{"given":"Qian","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Danting","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China"}]},{"given":"Kai","family":"Xian","sequence":"additional","affiliation":[{"name":"Beijing Transport Institute, Beijing 100073, China"}]},{"given":"Xuesong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Transport Institute, Beijing 100073, China"},{"name":"School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,15]]},"reference":[{"key":"ref_1","unstructured":"Lomax, T.J., and Schrank, D.L. 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