首页|Reports from King Fahd University of Petroleum and Minerals Advance Knowledge in Machine Learning (Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations)
Reports from King Fahd University of Petroleum and Minerals Advance Knowledge in Machine Learning (Machine Learning-Based Modeling of Celeration for Predicting Red-Light Violations)
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 – Investigators publish new report on ar tificial intelligence. According to news originating from Dhahran, Saudi Arabia, by NewsRx editors, the research stated, “This research examines the intricate c orrelation between speed variation (celeration), a metric of driver behavior ass ociated with vehicle control, and occurrences of running red lights.” Our news editors obtained a quote from the research from King Fahd University of Petroleum and Minerals: “The study is based on a thorough analysis of a large d ataset that includes a variety of parameters, such as exceeding speed limits, dr iver age, passenger count, weather, road condition, and temporal factors. Using cutting-edge machine learning methods like AdaBoost and Bagging, predictive mode ls for red-light violations are painstakingly built, achieving remarkable valida tion accuracies of 90.4 % and 90.1%, respectively. The study acknowledges the dataset’s limitations in capturing real-world traffic co mplexities while focusing on the effectiveness and trade-offs inherent in these methodologies. This emphasizes how important it is to have synchronized and thor ough data sources to guarantee accurate representation.”
King Fahd University of Petroleum and Mi neralsDhahranSaudi ArabiaAsiaCyborgsEmerging TechnologiesMachine Lea rning
NETL
NSTL
