首页|Research Findings from Vanderbilt University Update Understanding of Machine Lea rning (Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning)
Research Findings from Vanderbilt University Update Understanding of Machine Lea rning (Gauging road safety advances using a hybrid EWM-PROMETHEE II-DBSCAN model with machine learning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on artificial in telligence have been published. According to news reporting from Nashville, Tenn essee, by NewsRx journalists, research stated, "IntroductionEnhancing road safet y conditions alleviates socioeconomic hazards from traffic accidents and promote s public health. Monitoring progress and recalibrating measures are indispensabl e in this effort." The news journalists obtained a quote from the research from Vanderbilt Universi ty: "A systematic and scientific decision-making model that can achieve defensib le decision outputs with substantial reliability and stability is essential, par ticularly for road safety system analyses. MethodsWe developed a systematic meth odology combining the entropy weight method (EWM), preference ranking organizati on method for enrichment evaluation (PROMETHEE), and density-based spatial clust ering of applications with noise (DBSCAN)-referred to as EWM-PROMETHEE II-DBSCAN -to support road safety monitoring, recalibrating measures, and action planning. Notably, we enhanced DBSCAN with a machine learning algorithm (grid search) to determine the optimal parameters of neighborhood radius and minimum number of po ints, significantly impacting clustering quality. ResultsIn a real case study as sessing road safety in Southeast Asia, the multi-level comparisons validate the robustness of the proposed model, demonstrating its effectiveness in road safety decision-making. The integration of a machine learning tool (grid search) with the traditional DBSCAN clustering technique forms a robust framework, improving data analysis in complex environments. This framework addresses DBSCAN's limitat ions in nearest neighbor search and parameter selection, yielding more reliable decision outcomes, especially in small sample scenarios. The empirical results p rovide detailed insights into road safety performance and potential areas for im provement within Southeast Asia."
Vanderbilt UniversityNashvilleTennes seeUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesM achine Learning
NETL
NSTL
