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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">SSR</journal-id><journal-title-group><journal-title>Scientific and Social Research</journal-title></journal-title-group><issn>2661-4332</issn><eissn>2981-9946</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/ssr.v8i7.15783</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the Optimization of Logistics Distribution Path of Intelligent Transportation Big Data in Dalian Based on UESTC</title><url>https://artdesignp.com/journal/SSR/8/7/10.26689/ssr.v8i7.15783</url><author>ZhaoJun,XieJunqing</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>8</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-08-14</published-time></date></history><abstract>In order to solve the problem of urban traffic congestion and long peak time, this paper combines the characteristics of dynamic traffic congestion in Dalian and the actual scene of logistics distribution in the same city, taking into account the constraints of transportation cost, equipment loss cost, intelligent construction cost, distribution timeliness, and customer satisfaction, and taking the minimization of the total cost of global distribution as the core goal, a dynamic logistics distribution path optimization model based on traffic big data is constructed. The model is fully integrated into the quantitative indicators of road congestion in various periods and regions of Dalian, and solves the defects that the traditional static path model cannot adapt to tidal traffic and dynamic congestion, and adapts the improved genetic algorithm to solve the problem.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Huang QQ, 2026, Research On Logistics Distribution Network Optimization and Lean Management Mode Innovation Driven by Big Data. Logistics Engineering and Management, 48(4): 8–11.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Hu Z, 2026, Research on Cost Control of Unmanned Distribution of E-commerce Driven by Big Data. Business 2.0, 2026(9): 31–33.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Zou ZY, 2025, Research on the Application of Big Data and Cloud Computing in the Optimization of E-commerce Logistics Distribution. China Storage and Transportation, 2025(8): 210–211.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Shen XQ, 2024, Innovation Method of Information Model for Terminal Distribution of Logistics Enterprises in the Era of Big Data. China Storage and Transportation, 2024(11): 144–145.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Yu XS, 2023, Design and Implementation of the Background System of Fresh Food Distribution Platform Based on Big Data. Henan Science and Technology, 42(15): 30–33.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
