An Agent-based Approach for Modeling Real-time Travel Information in Transit Systems

Real-time travel information (RTI) systems are rapidly developed and deployed worldwide using the abundance of instantaneous data and dissemination means. This paper presents a framework for a multi-agent simulation model that emulates the generation and dissemination of RTI. The evolution of transit reliability influences both the performance of RTI generation schemes and the potential benefits that such information could yield. An iterative within-day network loading and a day-to-day learning process represent both service provider and service user ability to apply and adapt their strategies based on past performance and predictions. A case study illustrates model capabilities by applying BusMezzo, an agent-based simulation model of vehicles and travellers. The proposed model facilitates the analysis of alternative prediction schemes as well as the impact of their provision on system performance.