摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-调查人员讨论人工智能的新发现。根据NewsRx记者来自斯里兰卡莫拉图瓦的新闻，研究表明，"不断增长的城市人口和交通拥堵突出了建设行人友好环境的重要性，以鼓励步行作为首选交通方式"。这项研究的财政支持者包括屠维恩。新闻记者引用了莫拉特乌瓦大学的一项研究：“然而，一个主要的挑战仍然存在，那就是缺乏这种对行人友好的步行环境。识别高踩踏带浓度的位置和路线对于改善对行人友好的步行环境至关重要。本文提出了一种利用手机传感器实时数据绘制行人步行环境的定量方法。”本研究以斯里兰卡莫拉图瓦大学为例，综合了新的城市数据，如基于位置的服务(LBS)定位数据，以及无监督机器学习技术的数据聚类。本研究在实验研究背景下重点研究了以下三个量化步行行为的标准：步行速度、步行时间和步行方向。
Abstract
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 Moratuwa, Sri Lanka, by NewsRx correspondents, research stated, “The growing urban population and tr affic congestion underline the importance of building pedestrian-friendly enviro nments to encourage walking as a preferred mode of transportation.” Financial supporters for this research include Tu Wien. The news journalists obtained a quote from the research from University of Morat uwa: “However, a major challenge remains, which is the absence of such pedestria n-friendly walking environments. Identifying locations and routes with high pede strian concentration is critical for improving pedestrian-friendly walking envir onments. This paper presents a quantitative method to map pedestrian walking beh avior by utilizing real-time data from mobile phone sensors, focusing on the Uni versity of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, w alking time, and walking direction inside the experimental research context.”
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