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To remedy this problem, this study proposes a method for the microscopic traffic simulation calibration problem that considers the complexity of traffic conditions on-road sections and the differences in operating states between lanes. A simulation model was established by collecting actual data. Calibration parameters were determined using sensitivity analysis. A calibration model was built to minimize the relative errors of the roadway efficiency and lane differential indicators. The values of these parameters were obtained using a genetic algorithm (GA). The calibration processes were automated using programming. To assess the reliability of the proposed method, we conducted five sets of comparative experiments focusing on two aspects: calibration methods and algorithm utilization. Results indicate that the proposed method significantly enhances simulation accuracy, particularly in lane-level traffic simulations. In comparison to approaches considering only section-level traffic characteristics and default application software parameters, the proposed method yielded reductions in errors by 3.7%, 5.8%, 6.6%, and 3.2% for simulating lane occupancy rate and cross-section flow. The proposed method demonstrated a simulation error of approximately 5%, while the artificial neural network method was about 7%, validating the effectiveness of the algorithms employed. It can play a crucial role in multilane traffic flow, intelligent driving tests, vehicle\u2013road cooperation, and other related study areas.<\/jats:p>","DOI":"10.1177\/00375497241268740","type":"journal-article","created":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T07:02:10Z","timestamp":1723878130000},"page":"625-644","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Calibration method for microscopic traffic simulation considering lane difference"],"prefix":"10.1177","volume":"101","author":[{"given":"Huasheng","family":"Liu","sequence":"first","affiliation":[{"name":"Jilin University, China"}]},{"given":"Haoran","family":"Deng","sequence":"additional","affiliation":[{"name":"Jilin University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7463-830X","authenticated-orcid":false,"given":"Jin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Transportation Planning and Management, College of Transportation, Jilin University, China"}]},{"given":"Sha","family":"Yang","sequence":"additional","affiliation":[{"name":"Jilin University, China"}]},{"given":"Kui","family":"Dong","sequence":"additional","affiliation":[{"name":"Jilin University, China"}]},{"given":"Yuqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Jilin University, China"}]}],"member":"179","published-online":{"date-parts":[[2024,8,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2013.05.011"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.04.011"},{"key":"e_1_3_3_4_2","first-page":"41","volume-title":"The highway capacity manual","author":"Lily A","year":"2016","unstructured":"Lily A. 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