The topic of this paper is a solution to the problem of detecting traffic state in the urban environment, primarily the real-time location of public transport vehicles. The imagined solution involves users sharing their location and optionally other data such as the identifier of the vehicle in which they are currently driving in. Then, based on this data, the system determines the locations of vehicles, which allows further analysis to determine the time of arrival at individual stations. Unlike other solutions that could require significant investment in the city infrastructure, the only thing such a system needs is a mobile application, a server and a sufficient number of individuals willing to participate. The name of this technique is called crowdsensing. The paper deals with problems such as user grouping, detection of the route based on location history, detection of outliers in a group, etc. For the purposes of this paper, a simulation based on data obtained from GTFS files was developed. The analysis of these files yielded information such as travel times between pairs of stations for each tram, the distance between stations and lists of trams running on a particular station. In addition to the movement of trams, the simulation also includes the simulation of users riding these trams. In short, the simulation works by sequentially controlling trams which move by a method linear interpolation, stations that get notified by tram arrivals/departures and finally users who get randomly created at stations and wait for certain trams. The tram location prediction system receives only the location and optionally the tram identifier from the user, and on based on this data it groups users into groups representing trams and periodically checks the correctness of the groups.