首页|Study Data from University of Moratuwa Provide New Insights into Machine Learnin g (Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestria n Walking Behavior: A Case Study at the University of Moratuwa)
Study Data from University of Moratuwa Provide New Insights into Machine Learnin g (Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestria n Walking Behavior: A Case Study at the University of Moratuwa)
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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 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.”
University of MoratuwaMoratuwaSri La nkaCyborgsEmerging TechnologiesMachine Learning
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