Localizing Campus Shuttles from One Single Base Station Using LoRa Link Characteristics

Today more and more bus companies are providing real-time bus locations to their riders to improve passenger experience and increase ridership. Most of the existing bus localization systems rely on the Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS). However, it is costly to install GNSS receivers and retrofit existing buses to power them, which prevents them to be adopted by those bus operators with tight budgets. There has been increasing interest in developing GPS-free localization schemes that leverage the wireless signals transmitted by the buses to localize them. Such schemes often require the received signal strength (RSS) measured at multiple base stations and therefore are not applicable to a small transportation service with a single base station, such as the shuttle service for a university campus. This paper presents a novel approach that leverages the LoRa link characteristics measured by a single base station and deep learning to localize a campus shuttle when it approaches a stop. Experimental results show that our solution provides a detection accuracy of no less than 92.07% and significantly outperforms all baselines without requiring new hardware and introducing additional communication overhead.