Inferring Trips and Origin-Destination Flows From Wi-Fi Probe Data: A Case Study of Campus Wi-Fi Network

Abstract

This 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 Chiang Mai University campus as a city, we developed a framework 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, our contributions include algorithms 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 work. Our framework differs from the other studies with our 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. Our research underscores the significance of utilizing Wi-Fi probe data in mobility modeling. Additionally, it introduces an opportunistic sensing approach for estimating mobility flows, which not only contributes to our understanding of transportation dynamics but also holds significance in comprehending the implications for carbon capture efforts.

Keywords

• Human mobility
• origin-destination flow
• travel behavior
• trip inference
• urban informatics
• visual analytics tool