首页|Tongji University Reports Findings in Machine Learning (Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency a nd determinants in US)
Tongji University Reports Findings in Machine Learning (Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency a nd determinants in US)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Shanghai, People’s Rep ublic of China, by NewsRx correspondents, research stated, “Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial depend ence and spatial heterogeneity in the crash data. This study makes the best of G eographically Weighted Random Forest (GW-RF) model to explore the local associat ions between crash frequency and various influencing factors in the US, includin g road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources.”
ShanghaiPeople’s Republic of ChinaAs iaCyborgsEmerging TechnologiesMachine Learning
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