Development of prediction schemes for real-time bus arrival information.

Abstract: Intelligent Transport Systems (ITS) are increasingly used in public transport systems in order to provide real-time information (RTI) to passengers and operators. In particular, the RTI related to the prediction of remaining time until the arrival of the next vehicle is the most commonly provisioned information and the main focus of research. A number of predictions methods have been proposed without clear evidence of their real-world applicability, mainly because of their highly computational complexity. Moreover new sources of information, which could be used in RTI generators, become available but they have not been utilized yet. This thesis formulates a widely used real-world RTI generation meth-od, which is based on the scheduled travel time. Then, the potential contribution of real-time public transport data to RTI generation is investigated. Furthermore, a method that considers both the recent downstream running time information as well as anticipated headways and their impact on downstream dwell times is proposed. The generated predictions have to be compared against empirical bus arrival data in order to analyse the performance of the different schemes. Automatic Vehicle Location (AVL) data of the trunk bus network in Stockholm, were used for the evaluation of the proposed prediction schemes. The results illustrate the successful introduction of a robust methodology for bus arrival predictions, which outperforms the currently applied RTI generator. This methodology by integrating real-time public transport data is expected to reduce significantly passengers waiting time. In addition, the second proposed method provides a milestone for the incorporation of the dwell time component in the computation process of RTI.