Integrating SUMO and Kalman Filter Models Towards a Social Network Based Approach of Public Transport Arrival Time Prediction

Bus arrival time is a key service for improving public transport attractiveness by providing users with an accurate arrival time. In this research, a model of bus arrival time prediction, which aims to improve arrival time accuracy, is proposed. The arrival time will be predicted using a Kalman Filter (KF) model, by utilising information acquired from social networks. Social Networks feed road traffic information into the model, based on information provided by people who have witnessed events and then updated their social media accordingly. This research compares different KF models and identifies the best models to use for traffic prediction by employing traffic simulator, Simulation in Urban Mobility (SUMO). This paper discusses modelling a road journey using Kalman Filters and verifying the results with a corresponding SUMO simulation. Integrating the SUMO measures with the KF model can be seen as an initial step to verifying our premise that realtime data from social networks can eventually be used to improve the accuracy of the KF prediction. In order to acquire optimal estimation, verifying the trustworthiness of social network information is crucial. This paper discusses some ideas to establish a level of trust in social networks. This is important as KF model prediction will suffer if bogus information from social networks is used.