Lbs2its

Sri Lankan Delegates Participate in Erasmus + LBS2ITS Dissemination Conference at TU Wien, Vienna, Austria, 6 - 15 September 2024 - Embassy and Permanent Mission of Sri Lanka in Vienna A delegation of academics representing four prestigious Sri Lankan universities visited the Technische Universität Wien (TU Wien), in Vienna, Austria, from 6th to 15th September 2024 to participate in the Final Dissemination Conference relating to a project for Sri Lanka under the European Union’...

LBS2ITS Sri Lankan partners with Eugenio Delfino
(ERASMUS+ Project Advisor), Elisabetta Necco(Professional in Monitoring and Evaluation), Sewwandi Yapa, and at General Sir John Kotelawala Defence University.

Capturing Walking Speed Using Mobile Sensors and Machine Learning

The experience of capturing walking speed in and around the University of Moratuwa has been explored using real-time data from mobile phone sensors. This study focused on walking behavior on different road types near the University of Moratuwa, specifically examining walking speed, time, and direction.

The Sensor Logger app, QGIS, Google Colab (a cloud-based Jupyter Notebook environment for Python), and Microsoft Excel were utilized in this research. By applying K-means clustering, 622-speed clusters were identified during specific morning and evening hours. This method reveals mobility patterns, providing valuable insights into pedestrian walking behavior.

Additionally, a manual was prepared to guide others in capturing walking behavior. The guidance and support of Dr. Amila Jayasinghe, the supervisor, were instrumental in this research. The Location-based Services to Intelligent Transport Systems (LBS2ITS) project, funded by an Erasmus+ Grant, provided the necessary support and cutting-edge resources. This project offered an opportunity for technological exploration.

The tree mapping study at the University of Moratuwa was an invaluable experience for the team. Utilizing the S70G tablet with the Global Navigation Satellite System (GNSS) for high-accuracy GPS data collection, the trees were mapped precisely. The map preparation was conducted using QGIS 2.18. This marked the second study involving LBS applications for the researchers. This experience allowed the team to gain practical knowledge with cutting-edge geospatial technologies, integrating various data layers, performing spatial analysis, and producing detailed maps essential for environmental management and urban planning. The GNSS technology's accuracy guaranteed the reliability of the data, making it suitable for crucial decision-making processes.

In addition to the technical skills, the project culminated in the creation of a manual for tree mapping with the S70G tablet and GNSS technology. This opportunity was facilitated by the Location-based Services to Intelligent Transport Systems (LBS2ITS) project, which has been funded by an Erasmus + Grant (https://lbs2its.net/). The LBS2ITS project remains a crucial supporter in providing such technological exploration opportunities. The team expresses gratitude to LBS2ITS for providing these invaluable technological exploration opportunities. Thank you, LBS2ITS, for fostering such an enriching experience.
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