{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:03:04Z","timestamp":1787961784800,"version":"build-2784847793"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Sciences Foundation of the Ministry of Education","award":["17YJCZH260"],"award-info":[{"award-number":["17YJCZH260"]}]},{"name":"the CERNET Innovation Project","award":["NGII20180403"],"award-info":[{"award-number":["NGII20180403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Owing to frequent traffic accidents and casualties nowadays, the ability to predict the number of traffic accidents in a period is significant for the transportation department to make decisions scientifically. However, owing to many variables affecting traffic accidents in the road traffic system, there are two critical challenges in traffic accident prediction. The first issue is how to evaluate the weight of each variable\u2019s impact on the accident. The second issue is how to model the prediction process for multiple interrelated variables. Aiming to solve these two problems, we propose effective solutions to deal with traffic accident prediction. Firstly, for the first issue, we exploit the grey correlation analysis to measure the correlation of factors to accident occurrence. Then, for the second issue, we select the main factors by correlation analysis to establish a multivariable grey model\u2014MGM(1,N) for prediction process modeling. Further, we explore the collinearity between variables and better optimize the predictive model. The experimental results show that our approach achieves best performance than four general-purpose comparative algorithms in traffic accident prediction task.<\/jats:p>","DOI":"10.3390\/info11040184","type":"journal-article","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T13:27:19Z","timestamp":1585661239000},"page":"184","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Traffic Accident Prediction Based on Multivariable Grey Model"],"prefix":"10.3390","volume":"11","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TITS.2006.890070","article-title":"A traffic accident recording and reporting model at intersections","volume":"8","author":"Ki","year":"2007","journal-title":"IEEE Trans. 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