This paper presents a modeling framework that aims at assessing the impact of HSR service supply variations on other modes of transport, predicting on-board train volumes and, in particular, transfer flows at certain interchange nodes of the railway system. The framework, set to be a part of a larger Decision Support System (DSS) with the objective of optimizing High-speed train timetables and improving their interconnections with local services, consists of a sequence of multimodal transportation models (Cascetta and Coppola, 2012). The transportation services’ supply is modeled through a diachronic network (Nuzzolo and Russo, 1996) representing 15289 daily trains, of which 468 High-speed ones. Travel demand is estimated using a Nested Logit structure simulating the choice of the main mode (Car, Air, Bus, and Train) and for HSR also the choice of access/egress modes to/from stations. Finally, 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, mainly 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 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. With the increasing abundance of big data sources and the availability of open-source data in the transport sector, new opportunities are emerging for researching and studying advanced models to analyze and estimate current and future scenarios in the transportation sector. The case study analyzed is the national Italian multimodal transportation system, where multiple HSR companies operate in a competing (unsubsidized) 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 key nodes of the national rail network. 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. Socioeconomic Impacts of High-Speed Rail Systems - Proceedings of the 3rd International Workshop on High-Speed Rail Socioeconomic Impacts, University of Naples Federco II, Italy, International Union of Railways (UIC), 12–13 September 2023 (forthcoming) 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
Designing integration of High-speed Rail (HSR) and local rail services
Pietro Mariano;Fulvio Silvestri;Pierluigi Coppola
2024-01-01
Abstract
This paper presents a modeling framework that aims at assessing the impact of HSR service supply variations on other modes of transport, predicting on-board train volumes and, in particular, transfer flows at certain interchange nodes of the railway system. The framework, set to be a part of a larger Decision Support System (DSS) with the objective of optimizing High-speed train timetables and improving their interconnections with local services, consists of a sequence of multimodal transportation models (Cascetta and Coppola, 2012). The transportation services’ supply is modeled through a diachronic network (Nuzzolo and Russo, 1996) representing 15289 daily trains, of which 468 High-speed ones. Travel demand is estimated using a Nested Logit structure simulating the choice of the main mode (Car, Air, Bus, and Train) and for HSR also the choice of access/egress modes to/from stations. Finally, 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, mainly 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 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. With the increasing abundance of big data sources and the availability of open-source data in the transport sector, new opportunities are emerging for researching and studying advanced models to analyze and estimate current and future scenarios in the transportation sector. The case study analyzed is the national Italian multimodal transportation system, where multiple HSR companies operate in a competing (unsubsidized) 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 key nodes of the national rail network. 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. Socioeconomic Impacts of High-Speed Rail Systems - Proceedings of the 3rd International Workshop on High-Speed Rail Socioeconomic Impacts, University of Naples Federco II, Italy, International Union of Railways (UIC), 12–13 September 2023 (forthcoming) 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/10.1007/978-1-4757-6467-3I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
