Resumen
Incidents produce heavy congestion in large urban traffic networks and therefore real time information about them (e.g. location, timestamp, type) can be very useful for the drivers. An efficient way of gathering this type of information is through a crowd sourcing reporting system that multimodal travellers may utilise for providing information about various incidents they witness to other interconnected users in the same network. After the incoming traffic reports are evaluated, they can be shared to other travellers who are approaching the location of the reported incidents. Travelers can use the reported information for improving their mobility status. Collecting information using crowd sourcing techniques has implications and risks that need to be addressed. One of the most important challenges in this regard is the estimation of the reliability of the incoming information, usually related to individual user reputation. To this end, the exploitation of a reliability assessment system is of profound importance for assuring that only accurate information is shared between interconnected users. This paper introduces an innovative crowd sourcing information assessment mechanism for urban travellers. The purpose of the proposed probabilistic framework is to estimate if a user-generated report is true or false, given a set of static and dynamic parameters. The latter describe contextual conditions occurring at the time when an incident is reported. The proposed model takes into account the current location and speed of the reporting user due to their impact on the reliability of an incoming report. The proposed probabilistic model was evaluated in a simulation environment. Preliminary results show that, based on a set of rational assumptions, the estimated reliability decreases with the distance from the reported event and the speed of the reporting user. Based on the estimates that our model produces, a reliable true/false recommendation system can be devised for evaluating the user generated reports.