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This study presents a quantitative mapping approach using real-time mobile phone sensor data as a case at the University of Moratuwa, Sri Lanka. It integrates location-based service positioning with clustering and unsupervised machine learning to quantify walking speed, walking time, and walking direction. Signal processing identifies 622 speed clusters via K-means during morning and evening periods, supporting route and site classification to improve urban walking.","sensors   \nArticle  \nReal-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa  \nHarini Sawandi 1, Amila Jayasinghe 1 and Guenther Retscher 2, *  \nCitation: Sawandi, H.; Jayasinghe, A.; Retscher, G. Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior: A Case Study at the University of Moratuwa. Sensors 2024, 24, 3822. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s24123822  \nAcademic Editor: Valentina Agostini  \nReceived: 8 May 2024  \nRevised: 4 June 2024  \nAccepted: 7 June 2024  \nPublished: 13 June 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Town & Country Planning, University of Moratuwa, Moratuwa 10400, Sri Lanka; [harinisawandi@gmail.com](harinisawandi@gmail.com) (H.S.); [amilabj@uom.lk](amilabj@uom.lk) (A.J.)  \n2 Department of Geodesy and Geoinformation, TU Wien—Vienna University of Technology, 1040 Vienna, Austria  \n* Correspondence: [guenther.retscher@tuwien.ac.at](guenther.retscher@tuwien.ac.at)  \nAbstract: The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University 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, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.  \nKeywords: walking behavior; mobile GPS tracking; machine learning; pedestrian-friendly environment  \n1. Introduction  \nInvestigating pedestrian behavior and improving walking space in streets are becoming increasingly crucial considering the proven benefits to health, sustainability, and the development of safer pedestrian-friendly areas [1,2] . Consequently, an increasing amount of research has been carried out examining the relationship between the urban environment and individuals’behavior on streets [3] . However, many of these studies focus on the macro level; in addition to considering the urban characteristics on a wider scale, it is important to also consider microscale factors of urban design that influence behavior on streets [1] . This requires collecting data on the micro-level walking behavior of individuals on the streets to obtain precise information and develop target solutions for promoting walking.  \nBehavior mapping is a commonly utilized technique for the direct and systematic monitoring of individual behaviors and locations [1] . This mapping was first used in indoor locations, primarily in the fields of psychology, sociology, and criminology. It ha","cbCaidrqtfXG39ZK","https://ap.wps.com/l/cbCaidrqtfXG39ZK","pdf",12984555,2,1,16,"English","en",105,"# Introduction\n## Behavior mapping and urban design context\n## Real-time tracking technologies and GPS/LBS\n# Methodology\n## Data sources and criteria for quantifying walking behavior\n## Signal processing and clustering approach\n# Results and analysis\n## Speed clustering with K-means\n# Conclusion\n## Implications for mapping and improving pedestrian environments","[{\"question\":\"Why is mapping pedestrian walking behavior important for urban planning?\",\"answer\":\"Mapping helps improve pedestrian-friendly environments by identifying locations and routes with high pedestrian concentration and understanding movement patterns that support safer, healthier walking spaces.\"},{\"question\":\"What data and techniques are used in this case study?\",\"answer\":\"The study uses real-time mobile phone sensor data, including location-based service positioning, combined with clustering and unsupervised machine learning methods to quantify walking behavior.\"},{\"question\":\"How is walking behavior quantified in the study?\",\"answer\":\"Walking speed, walking time, and walking direction are quantified within the experimental context, supported by signal processing of speed signals.\"},{\"question\":\"What is the key finding regarding speed patterns?\",\"answer\":\"A speed signal processing workflow identifies 622 speed clusters using K-means clustering during selected morning and evening hours.\"}]","Real-Time Tracking Data and Machine Learning Approaches for Mapping Pedestrian Walking Behavior - 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