Dynamic Forecast Model of Time Dependent Passenger Flows for Disruption Management

The focus of recent research on disruption management in public transportation networks more and more shifts from operator-related aspects to improving the service quality. It is, however, not yet well understood how the passenger demand changes in a disrupted situation. Forecasts of passenger demand traditionally focus on the long-term. Forecasts for disruption management on the contrary require detailed short-term predictions. However, knowledge on passenger demand is generally lacking. Detailed information on passenger demand was not available until the introduction of smart card systems. Then again this data in its current form is not suitable for forecasting. In this paper we propose a new algorithm that, based on the characteristics of the disruption, transforms the smart card data to suitable data for forecasting real-time passenger demand as required for disruption management. This is a new way of estimating demand for disruption management as well as a novel application of smart card data. Evelien van der Hurk Department of Decision and Information Sciences, Rotterdam School of Management Erasmus University, Rotterdam, the Netherlands Tel.: +31-10-4082314 E-mail: [email protected] Leo Kroon Department of Decision and Information Sciences, Rotterdam School of Management Erasmus University, Rotterdam, the Netherlands Gábor Maróti Innovation and Process Quality, Netherlands Railways Utrecht, The Netherlands Peter Vervest Department of Decision and Information Sciences, Rotterdam School of Management Erasmus University, Rotterdam, the Netherlands 2 Evelien van der Hurk et al. We present preliminary results for the Dutch passenger rail operator Netherlands Railways (NS).