RoadNote: Automated Road Closure Detection using Urban Sensing

Abstract

Maps and navigation applications are essential tools in the modern era, especially for smartphone users. Navigation apps not only guide us on the correct path to the destination but also serve to find convenience and provide connectivity by sharing locations, travel status, and expert guidance. Map applications offer real-time updates which rely on crowdsourcing data from users, historical data, and advanced prediction algorithms. However, due to the dynamic nature of the urban environment, navigational apps fail to provide unscheduled road closure information. This study investigates erroneous situations and found 23 incidences where maps fail to navigate the closure information. We propose ROADNote, an automated system that accommodates urban sensors and provides closures update to users. ROADNote provides real-time traffic conditions by automated detections using future-generation commodity sensors. We built a prototype of ROADNote; after that, we conducted experiments to get real-time road-closer information by visual sensors (i.e., drone, camera). ROADNote facilitates to reduce of average travel time by 3.48 minutes and distance by more than 300 meters.