Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis of Micro-mobility Services

Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular among urban communities, being a flexible transport option that brings both opportunities and challenges. As a growing mode of transportation, insights gained from micro-mobility usage data are valuable in policy formulation and improving the quality of services. Existing research analyses patterns and features associated with usage distributions in different localities, and focuses on either temporal or spatial aspects. In this paper, we employ a combination of methods that analyse both spatial and temporal characteristics related to e-scooter trips in a more granular level, enabling observations at different time frames and local geographical zones that prior analysis wasn't able to do. The insights obtained from anonymised, restricted data on shared e-scooter rides show the applicability of the employed method on regulated, privacy preserving micro-mobility trip data. Our results showed population density is the topmost important feature, and it associates with e-scooter usage positively. Population owning motor vehicles is negatively associated with shared e-scooter trips, suggesting a reduction in e-scooter usage among motor vehicle owners. Furthermore, we found that the effect of humidity is more important than precipitation in predicting hourly e-scooter trip count. Buffer analysis showed, nearly 29% trips were stopped, and 27% trips were started on the footpath, revealing higher utilisation of footpaths for parking e-scooters in Melbourne.Comment: Accepted to IEEE International Conference on Mobile Data Managemen

ZzzGPT: An Interactive GPT Approach to Enhance Sleep Quality

In today's world, sleep quality is pivotal for overall well-being. While wearable sensors offer real-time monitoring, they often lack actionable insights, leading to user abandonment. This paper delves into the role of technology in understanding sleep patterns. We introduce a two-stage framework, utilizing Large Language Models (LLMs), aiming to provide accurate sleep predictions with actionable feedback. Leveraging the GLOBEM dataset and synthetic data from LLMs, we highlight enhanced results with models like XGBoost. Our approach merges advanced machine learning with user-centric design, blending scientific accuracy with practicality