Wi-Finding: Urban Transportation Sensing Using Crowdsourced Wi-Fi

Knowing which transportation modes citizens use is critical for smart cities and planners. We show that scanning pervasive Wi-Fi access points with mobile phones can enhance GPS and geographic information to improve transportation detection and identify public transportation modes while conserving battery. This approach yields an average F1 score of 85% for inferring ve common modes. Wi-Fi features improve detection accuracy of bus trips, train travel, and driving compared to GPS features alone and can substitute for GIS features. We conclude that crowdsourced Wi-Fi has been underutilized in transportation research and can improve mobile travel surveys and urban sensing applications.

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