Performance evaluation of a live, crowdsensing based transit feed service architecture

Abstract—Taking public transportation is an efficient and environmentally sound way of traveling in most of the cities. Unfortunately, the static schedule information of the public transport vehicles, available at the stops, on the web or in some special format like GTFS (General Transit Feed Specification), usually does not reflect the actual traffic situation. However, realtime traffic updates require the gathering of an immense amount of tracking data. Mobile crowdsensing, via the mobile devices of the crowd, can offer a cheap and efficient way for collecting such data. Nonetheless, for motivating active participation some day zero service must be provided to the users. In this paper, we discuss how to realize a live public transport information service extending a static timetable based on GTFS data, as the day zero service, using the power of the crowd for data collection. We detail the design of such a service implemented by an XMPP (Extensible Messaging and Presence Protocol) based mobile crowdsensing architecture and evaluate its performance. We show how we can build a scalable service architecture even with commodity hardware to handle thousands of users.