Intelligent Transportation Systems allows sensors and GPS devices to monitor public transport systems in Smart Cities. Capturing and processing this data should, in theory, allow systems to make the public transport more reliable and predictable for the citizens, which would improve the quality of life of the urban population and the environment. Insufficient or low-quality data, nevertheless, may prevent its use on such real-time systems. This work studies the use of data obtained from crowdsourcing as an alternative to augment this data. In order to mitigate the uncertainties introduced by the crowdsourced data, this work proposes a reliability model for crowdsourced data conceived for the São Paulo bus-based public transport system.
