首页|La Trobe University Reports Findings in Machine Learning (A novel framework for crash frequency prediction: Geographic support vector regression based on agent- based activity models in Greater Melbourne)
La Trobe University Reports Findings in Machine Learning (A novel framework for crash frequency prediction: Geographic support vector regression based on agent- based activity models in Greater Melbourne)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating from Bundoora, Au stralia, by NewsRx correspondents, research stated, “The fieldof spatial analys is in traffic crash studies can often enhance predictive performance by addressi ng theinherent spatial dependence and heterogeneity in crash data. This researc h introduces the GeographicalSupport Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assessspatial variations and evalu ate the influence of a wide range of factors, including traffic, infrastructure,socio-demographic, travel demand, and land use, on the incidence of total and f atal-or-serious injury (FSI)crashes across Greater Melbourne’s zones.”
BundooraAustraliaAustralia and New Z ealandCyborgsEmerging TechnologiesMachine LearningSupport Vector Regress ion
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