摘要
由一名新闻记者-机器人与机器学习每日新闻编辑-研究人员详细介绍了人工智能的新数据。根据NewsRx Jour Nalists在约旦伊尔比德的新闻报道，研究表明：“这项研究采用机器学习(ML)技术来预测约旦城市环境下人行横道的行人合规性，以加强行人安全和交通管理。”新闻记者从Yarmouk University的研究中获得了一句话：“利用安曼、伊尔比德和扎尔卡信号交叉口2437名行人的数据，基于不同的ML算法开发了四个模型：N人工神经网络(ANN)，支持向量机(SVM)，决策TR EE(ID3)，结果表明，局部基础设施和交通条件对行人行为有影响，RF模型具有良好的精度和准确性，是准确预测行人行为的理想选择，为研究影响行人遵守当地环境法规的人口和空间因素提供了有价值的见解。"该工作提高了ML算法改善城市交通动力学的能力."
Abstract
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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