
1. INTRODUCTION
• Title of the dataset
	o Data underlying the publication: Service Design and Frequency Setting for the European High-Speed Rail Network
• Description of the research
	o We present a customised version of the Transit Network Design and Frequency Setting Problem (TNDFSP) for the long-distance transport context and HSR in particular. This allows to asses the potential of design and policy choices for HSR network design. We apply an adapted version of a heuristic solution approach to analyse the users’, operators’ and societal performance of a European HSR-network by conducting an extensive series of experiments to test the network’s performance under various policy priorities and HSR design variables.  This dataset contains the two python scripts used: the first to estimate long-distance travel demand and secondly our heuristic solution approach to solve the TNDFSP. Also, input data is included , which allows to run the scripts for new experiments
• Document descriptions (demand estimations)
	o Module_Demand_Estimation (Grolle 2023).py ; script for estimating long-distance travel demand
	o Airportinformation3.xlsx ; data of worldwide airports
	o AP_data / AP_data_freq.xlsx ; Number of passengers and number of flights for European airports, based in the ‘avia_par_xx.xlsx’ files
	o #City_to_Airport_access_and_egress.xlsx ; Overview of access/egress times for European cities and associated airports within a 5-hour drive
	o #Core_cities_geography.xlsx ; Overview of travel times and distances for different modes between the selection of 125 cities
	o #European_AirportToAirport_Traffic_2019.xlsx ; OD matrices of passenger flows between European and peripheral airports, as reported by ‘EU member states+’ and mirrored for one-sided reporting
	o #European_CityToCity_Traffic_2019.xlsx ; Air traffic projected on several European cities and then extrapolated to total traffic flows.
	o Avia_par_xx ; registered passenger movements
• Document descriptions (demand estimations)
	o Module_Heuristic_Solution (Grolle 2023).py ; script for estimating long-distance travel demand
	o control_panel_large.xlsx ; collected data of demand estimations and generalised geographic data required for modelling

2. METHODOLOGICAL INFORMATION
• All data is extraced from publically available sources, as also noted in relevant documents itself. 

3. DATA SPECIFIC INFORMATION
• All excel imput documents have been assigned full names and definitions of column headers for tabular data, units of measurement and definitions

SHARING AND ACCESS INFORMATION
• All data is published under the 4TU Data Repository CC0 license.
