دورية أكاديمية

Rome vehicle accidents analysis and city riskiness prediction by Artificial Intelligence

التفاصيل البيبلوغرافية
العنوان: Rome vehicle accidents analysis and city riskiness prediction by Artificial Intelligence
المؤلفون: Giuliano Muratore, Aldo Vannelli, Davide Micheli
المصدر: Transportation Engineering, Vol 12, Iss , Pp 100172- (2023)
بيانات النشر: Elsevier, 2023.
سنة النشر: 2023
المجموعة: LCC:Transportation engineering
مصطلحات موضوعية: Vehicle’ accidents, Random forest, Rome, Smart city, Road safety, Management decisions, Transportation engineering, TA1001-1280
الوصف: Rome municipality publishes geo-referenced vehicle accidents reports and detailed road traffic counters that, in conjunction with Open Street Map roads details, enable deep analyses of the tragic phenomenon. This study focuses on the full year 2021, during which a total of 62,081 vehicles casualties were registered by Rome traffic authorities. The broad Rome territory, overcoming a bunch of Italian chief towns, the wide and capillary set of arteries and the municipality roads extension greater with respect to many European capitals, enhance the statistical relevance of the analyzed data. Artificial intelligence method takes into account three potential accidents predictors as the level of road traffic, the event timing, and the driving complexity of the different zones. The proposed method has the capability of linking time/traffic/complexity indexes of a specific zone of a city with the accident riskiness, moreover, is applicable to whatever urban complex scenario and takes care of the result format in order to facilitate road safety decision makers in smoothing the tragic sequence of accidents. Further possible improvements directions are discussed.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2666-691X
Relation: http://www.sciencedirect.com/science/article/pii/S2666691X2300012X; https://doaj.org/toc/2666-691X
DOI: 10.1016/j.treng.2023.100172
URL الوصول: https://doaj.org/article/56820aadf40641f2aa4ff4a58ac88526
رقم الأكسشن: edsdoj.56820aadf40641f2aa4ff4a58ac88526
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:2666691X
DOI:10.1016/j.treng.2023.100172