Modeling High-speed Rail (HSR) service integration and timetable optimization

This paper presents a schedule-based modeling framework, consisting of a sequence of multimodal transportation models (Cascetta and Coppola, 2012), aiming at predicting on-board (single) train flows and at assessing the impact of timetable variations on national and regional travel demand, particularly transfer flows at the main interchange HSR station. This is part of a Decision Support System (DSS) for High-speed train timetables setting and improving their interconnections with local services. Transportation supply is modeled through a diachronic network (Nuzzolo and Russo, 1996) representing 15’289 daily trains, of which 468 High-speed ones; 3754 flights and 4514 bus services over a single day. Car network graph consists of 978’186 links. Travel demand is estimated using a Nested Logit structure simulating the choice of the main mode (Car, Air, Bus, and Train). For HSR is also simulated the choice of access/egress modes to/from stations. A schedule-based assignment model (Wilson and Nuzzolo, 2004) allows to generate a set of feasible route choices alternative for each origin-destination pair and each hour of the day, and to estimate the passenger flows on each individual train (Silvestri et al., 2024). Data used for modeling include, on the supply side, the General Transit Feed Specification (GTFS) files describing the Air, Train and Bus services and the OpenStreetMap GIS-database for developing the road network. Mobile Network Operator data (Willumsen, 2021) are used to model the demand side, leveraging diverse other sources for models’ calibration and results validation, including ticket sales on HSR specific trains, vehicle counts on highways, and passenger volumes at airports. The case study analyzed is the national Italian multimodal transportation system, where multiple operators compete for the HSR (unsubsidized) market (i.e., competition within Rail market). Several scenarios are presented exploring the potential of the proposed modeling approach to support the optimization of the connections between local and HSR services at the main nodes of the national rail network and to assess the overall impact on the Rail market (i.e., competition for the Rail market). References Cascetta, E., Coppola, P., 2012. An elastic demand schedule-based multimodal assignment model for the simulation of high speed rail (HSR) systems. EURO J. Transp. Logist. 1, 3–27. https://doi.org/10.1007/s13676-012-0002-0 Nuzzolo, A., Russo, F., 1996. Stochastic Assignment Models for Transit Low Frequency Services: Some Theoretical and Operative Aspects, in: Bianco, L., Toth, P. (Eds.), Advanced Methods in Transportation Analysis, Transportation Analysis. Springer, Berlin, Heidelberg, pp. 321–339. https://doi.org/10.1007/978-3-642-85256-5_14 Silvestri, F., Montino, T., Mariano, P. (2024). Estimating Schedule-Based Assignment Models for High-Speed Rail (HSR) Services Using Multiple Data Sources. In: Pagliara, F. (eds) Socioeconomic Impacts of High-Speed Rail Systems. IW-HSR 2023. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-031-53684-7_7 Willumsen, L., 2021. Corporate Partnership Board CPB Use of Big Data in Transport Modelling Discussion Paper. Wilson, N.H.M., Nuzzolo, A. (Eds.), 2004. Schedule-Based Dynamic Transit Modeling: theory and applications, Operations Research/Computer Science Interfaces Series. Springer US, Boston, MA. https://doi.org/10.1007/978-1-4757-6467-3