Transportation Research Part A

The study area includes 65 BRI countries. However, due to limited data availability, only 64 countries were used to analyze the economic growth (PCGDP), 62 countries were used to analyze the rail infrastructure (RAIL), and 49 countries were used to analyze the road infrastructure (ROAD) in the analysis of the spatial-temporal characteristics and spatial autocorrelation. Among the BRI countries, only 42 countries have simultaneous data on RAIL, ROAD, and PCGDP. The existing spatial econometrics are codes for balanced panel data. Therefore, only 42 countries are selected for the empirical study.

The original statistical data, lower level model and upper level model statistical data, and lower level model and upper level model bivariate correlation analysis results

Correlation matrices

The given data file consists of various computational steps and summary of data used in the present manuscript. The average grey matrixes, crisp matrices, and normalised direct crisp matrices.

Supplementary Data A - Matlab Code for Charging Time Estimation Supplementary Data B - Info of Normal Charging Stations and ETEC Distribution Results Supplementary Data C - Info of Fast Charging Stations and ETEC Distribution Results

This provides information about Crime (as measured by NIBRS) and Uber entry by city.

Detail information on 416 Uber and Lyft rides (times, distances, passenger cost, driver earnings) provided by one of the authors during the Fall 2016 covering the Denver Metropolitan area

A survey of Seattle bikesharing users and potential users was conducted in February - March 2018. Respondents included users of Pronto (222 respondents) and dockless bikesharing (505 respondents), and potential Seattle bikesharing users who had not yet used any Seattle bikesharing offerings. The survey was hosted on Google Forms and was distributed through several online platforms: UW Today / UW News, the Seattle Bike Blog, and social media. As a participation incentive, respondents were entered in a drawing for a premium bag (valued at $160) from Swift Industries, a Seattle company. The survey included sections on Pronto, dockless bikesharing, and demographics.

This is the data used in the paper 'Impact of High-speed Rail on Population Mobility and Urbanisation: A case study on Yangtze River Delta Urban Agglomeration, China'. The file includes data of population, urbanization, economic development, and high-speed rail constration of the 26 cities of Yangtze River Delta urban agglomeration in China from 2005 to 2016. The data of high-speed rail are from the China Railway Yearbook from 2006 to 2017, and the data for high-speed rail service frequency are from the National Railway Timetable released by the Ministry of Railways of the People's Republic of China over 2005–2016. Other data are collected from the China City Statistical Yearbook, and provincial and urban statistical yearbooks. The third table in the file shows the calculation methods of the processed data.

Model results for structural equation modelling of passenger satisfaction across six European cities.