การพัฒนาระบบเพื่ออนุมานรูปแบบการเดินทางโดยใช้ข้อมูลสัญญาณเครือข่ายคอมพิวเตอร์ไร้สาย

Abstract

This thesis work introduces an alternative solution to costly conventional approaches for large-scale travel behavior data collection by utilizing an opportunistic sensing data source i.e., Wi-Fi probe data. Through our case study of campus as a city, a framework has been developed for inferring and visualizing Wi-Fi data-based travel behavior by demonstrating how a Wi-Fi probe data can be analyzed to infer trips and origin-destination flows. Specifically, algorithms are developed for inferring spatial presence, residence, stay, trip, and trip distribution among places in the campus, as well as campus inflow and outflow. Moreover, to handle the Wi-Fi access point data for the analysis, and visualize the inferred trips and flows, an online visual analytics tool called Wi-Flow is developed as part of this research. The developed framework differs from the other studies with newly developed residence and trip detection algorithms that produce the result at the individual level as opposed to the overall network. The experimental results are intuitive and insightful, providing useful information for area management. This thesis work highlights the usefulness of Wi-Fi probe data in mobility modeling, and, in general, paves the way for opportunistic sensing approach to estimating mobility flows.