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However, researches on what, and how factors influence passenger train occupation rate remain sparse. Based on the collective data of 363 passenger trains on the Beijing-Shanghai High-speed Railway in November 2015, this paper establishes a data-driven analysis framework to explore the influencing factors of passenger train occupation rate. Specifically, we first analyze the possible factors that influence train occupation rate from the perspective of train service planning. Then, the approach of association rules is applied to analyze the potential relationship between train occupation rate and its influencing factors. And a total of 6711 and 8133 association rules were generated for high and low train occupation rate of passenger trains, respectively. Further analysis found that train departure and arrival times, train departure and arrival station class and the type of trains are the main factors influencing the level of train occupation rate. These findings can provide reference for train service planning.<\/jats:p>","DOI":"10.3233\/jifs-179663","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T07:58:57Z","timestamp":1582271937000},"page":"5753-5761","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring passenger train occupation rate influencing factors using association rules: A case study in China"],"prefix":"10.1177","volume":"38","author":[{"given":"Changan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"},{"name":"National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China"}]},{"given":"Sihan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"},{"name":"National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China"}]},{"given":"Heying","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"},{"name":"National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China"}]},{"given":"Shaoquan","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"},{"name":"National Railway Train Diagram Research and Training Center, Southwest Jiaotong University, Chengdu, China"},{"name":"National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation, Southwest Jiaotong University, Chengdu, China"}]}],"member":"179","published-online":{"date-parts":[[2020,2,21]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/056943457201600202"},{"key":"e_1_3_2_3_2","unstructured":"Fr\u00f6idhO. 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