首页|New Machine Learning Study Results from Ahsanullah University of Science and Tec hnology (AUST) Described (Investigating factors influencing pedestrian crosswalk usage behavior in Dhaka city using supervised machine learning techniques)
New Machine Learning Study Results from Ahsanullah University of Science and Tec hnology (AUST) Described (Investigating factors influencing pedestrian crosswalk usage behavior in Dhaka city using supervised machine learning techniques)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news originating from Dhaka, Bangladesh, b y NewsRx correspondents, research stated, “Pedestrians are the most vulnerable r oad users and are over-represented in casualty statistics, particularly in low- and middle-income countries like Bangladesh.” The news editors obtained a quote from the research from Ahsanullah University o f Science and Technology (AUST): “To ensure the safety of pedestrians, it is nec essary to identify the factors underlying pedestrian behavior while crossing. He nce, this study aims to predict the pedestrian decision regarding crosswalks usi ng supervised machine learning techniques namely, Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boost (XGBoost). A questio nnaire survey was conducted in twelve important locations of Dhaka, Bangladesh u sing 8 attributes related to crosswalk behavior. Analysis suggests RF model is t he most effective in terms of prediction performances, specifically having a 96. 00% F1 score and 95.83% MCC value. It has been found that unsuitability of crosswalk location, absence of guard rails on median, and inadequate lightning at night near crosswalks are the most important features f or preferring to use crosswalks.”
Ahsanullah University of Science and Tec hnology (AUST)DhakaBangladeshAsiaCyborgsEmerging TechnologiesMachine Learning
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