Demand for Intercity Passenger Rail (IPR) transportation in the US continues to grow as the National Railroad Passenger Corporation (Amtrak) sets record annual ridership figures year after year. Since forecasted ridership is critical to developing and evaluating proposals for new or expanded IPR service across the country, an improved understanding of what factors drive origin destination (O-D) passenger travel demand for IPR service is essential for planners, stakeholders, and operators. The O-D of intercity passenger train riders can be obtained from ticket sales, but comprehensive O-D US IPR ridership data, or the trip purposes of riders – which drives their
travel decision making – are not. Therefore, additional analysis and modeling is required to make the leap from station-level demand to ridership along each segment of an IPR corridor and to tailor and market intercity rail services to maximize ridership for any given level of investment.
This research addresses this gap through two methodological approaches. Nationally, it leverages various metrics for station-level ridership, corridor passenger-miles, average trip lengths, and the relative ridership rankings of O-D station pairs that are publicly available for various US IPR corridors and services. The work will be complemented by a detailed collection of ridership data on the three intercity passenger services offered in the State of Michigan, first as an online survey accessed via mobile devices to be completed by passengers during active train rides, and then through in-depth analysis of in-person interviews by Michigan Tech researchers with passengers at rail stations and aboard the train. A geospatial analysis will be conducted with the survey results to identify the origins, destinations, and travel mode choices for riders to and from rail stations.
The results from this research can provide stakeholders involved in the development of passenger rail corridors a better understanding of ridership factors compared to the limitations of the current station-only approach. The comparison of modeling framework with actual case study outcomes helps to build confidence in the model and the resulting model will help inform trade-offs between making investments to add frequencies or infill stations to existing services or developing new routes and corridors.