{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T14:42:13Z","timestamp":1781880133331,"version":"3.54.5"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"S&amp;T Special Program of Huzhou and Philosophy","award":["2024GZ27"],"award-info":[{"award-number":["2024GZ27"]}]},{"name":"S&amp;T Special Program of Huzhou and Philosophy","award":["2024SJYB0142"],"award-info":[{"award-number":["2024SJYB0142"]}]},{"name":"Social Science Project of Colleges and Universities in Jiangsu Province","award":["2024GZ27"],"award-info":[{"award-number":["2024GZ27"]}]},{"name":"Social Science Project of Colleges and Universities in Jiangsu Province","award":["2024SJYB0142"],"award-info":[{"award-number":["2024SJYB0142"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Previous studies on highway_rail grade crossing collisions have primarily focused on identifying factors contributing to the frequency and severity of driver injuries. In recent years, increasing attention has been given to modeling driver injury severity at these crossings. Recognizing the variations in injury severity under different road surface conditions, this study investigates the impact of road surface conditions on driver injury severity at highway_rail grade crossings. Using nearly a decade of accident data (2012\u20132021), thi study employs a LightGBM model to predict factors influencing injury severity and utilizes SHAP values for result interpretation. The symmetry principle of SHAP esures that factors with identical influence receive equal values, enhancing the reliability of predictive outcomes. The findings reveal that driver injury severity at highway_rail grade crossings varies significantly under different road surface conditions. Key factors identified include train speed, driver age, vehicle speed, annual average daily traffic (AADT), driver presence inside the vehicle, weather conditions, and location. The results indicate that collisions are more frequent when either the vehicle or train travels at high speed. Implementing speed limits for both vehicles and trains under varying road conditions could effectively reduce accident severity. Additionally, older drivers are more prone to severe accidents, highlighting the importance of installing control devices, such as warning signs or signals, to enhance driver alertness and mitigate injury risks. Furthermore, adverse weather conditions, such as rain, snow, and fog, exacerbate accident severity on road surfaces like sand, mud, dirt, oil, or gravel. Timely removal of surface obstacles may help reduce the severity of such accidents.<\/jats:p>","DOI":"10.3390\/sym17010147","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T09:11:13Z","timestamp":1737364273000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Analysis of the Impact of Different Road Conditions on Accident Severity at Highway-Rail Grade Crossings Based on Explainable Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Zhen","family":"Yang","sequence":"first","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5535-6467","authenticated-orcid":false,"given":"Gen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyi","family":"Xu","sequence":"additional","affiliation":[{"name":"Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1080\/19427867.2022.2086760","article-title":"Analysis of injury severity in rear-end crashes on an expressway involving different types of vehicles using random-parameters logit models with heterogeneity in means and variances","volume":"15","author":"Wang","year":"2023","journal-title":"Transp. 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