Exploring the potential of open big data from ticketing websites to characterize travel patterns within the Chinese high-speed rail system

Abstract

Big data have contributed to deepen our understanding in regards to many human systems, particularly human mobility patterns and the structure and functioning of transportation systems. Resonating the recent call for ‘open big data,’ big data from various sources on a range of scales have become increasingly accessible to the public. However, open big data relevant to travelers within public transit tools remain scarce, hindering any further in-depth study on human mobility patterns. Here, we explore ticketing-website derived data that are publically available but have been largely neglected. We demonstrate the power, potential and limitations of this open big data, using the Chinese high-speed rail (HSR) system as an example. Using an application programming interface, we automatically collected the data on the remaining tickets (RTD) for scheduled trains at the last second before departure in order to retrieve information on unused transit capacity, occupancy rate of trains, and passenger flux at stations. We show that this information is highly useful in characterizing the spatiotemporal patterns of traveling behaviors on the Chinese HSR, such as weekend traveling behavior, imbalanced commuting behavior, and station functionality. Our work facilitates the understanding of human traveling patterns along the Chinese HSR, and the functionality of the largest HSR system in the world. We expect our work to attract attention regarding this unique open big data source for the study of analogous transportation systems.

Examining the population flow network in China and its implications for epidemic control based on Baidu migration data

“…These studies have explored geographic borders of human mobility, individual human mobility patterns, travel behaviour, and the spatial structure of cities (Gariazzo and Pelliccioni, 2019;Lee et al, 2018;Picornell et al, 2015;Sagl et al, 2014;Louail et al, 2014;Rinzivillo et al, 2012). As mobile phone data are generally expensive and there are privacy concerns, researchers encounter many obstacles in the collection of large amounts of data for use in large-scale spatial population flow analysis (Wei et al, 2017). Fortunately, open big data sources provide new research opportunities and increase the credibility of the results.…”

Hierarchical structure in the world’s largest high-speed rail network

“…Our findings could have useful implications for future studies on this strikingly unique PTS. An implication can be linked to the consequences of socioeconomic development, since there has been evidence that the HSR has a positive effect on the economy at the city level [31,48]. If this effect can be indeed pronounced countrywide, a corollary is that the hierarchical structure of HSR would cascade to the entire social system, potentially influencing distribution (e.g., rank-size distribution of cities), inequality, and other aspects of society.…”

“…The potential of open big data is just beginning to be explored. For example, freely accessed information on remaining tickets from ticketing websites can be assimilated and synthesized for retrieving passenger flow and occupancy rates, and thereby depicting PTS functions [31]. This information is also valuable for characterizing the holistic spatial structure of PTS networks [32].…”

“…Despite their relatively short time period of existence, Chinese HSRs are expected to profoundly influence Chinese socio-geographical systems in many aspects [34–37]. However, so far, our understanding of the world’s largest HSR system remains limited (but see [31,32]).…”

Big Spatiotemporal Data Analytics: a research and innovation frontier

“…Open or derived data are especially useful, e.g. bullet train data in China were used to analyze travel behavior and station capability (Wei et al 2017).…”