首页|Research from Yarmouk University in Machine Learning Provides New Insights (Usin g Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan)
Research from Yarmouk University in Machine Learning Provides New Insights (Usin g Machine Learning to Predict Pedestrian Compliance at Crosswalks in Jordan)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting from Irbid, Jordan, by NewsRx jour nalists, research stated, "This study employs machine learning (ML) techniques t o predict pedestrian compliance at crosswalks in urban settings in Jordan, aimin g to enhance pedestrian safety and traffic management." The news correspondents obtained a quote from the research from Yarmouk Universi ty: "Utilizing data from 2437 pedestrians at signalized intersections in Amman, Irbid, and Zarqa, four models based on different ML algorithms were developed: a n artificial neural network (ANN), a support vector machine (SVM), a decision tr ee (ID3), and a random forest (RF). The results have shown that local infrastruc ture and traffic conditions influence pedestrian behavior. The RF model, with it s excellent accuracy and precision, has proven to be an excellent choice for acc urately predicting pedestrian behavior. This research provides valuable insights into the demographic and spatial aspects that influence pedestrian compliance w ith laws and regulations in the local environment. Additionally, this work highl ights the ability of ML algorithms to improve urban traffic dynamics."
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