Dataset Information

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.

SUBMITTER: Wei S

PROVIDER: S-EPMC5456051 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Wei Sheng S Yuan Jinfu J Qiu Yanning Y Luan Xiali X Han Shanrui S Zhou Wen W Xu Chi C

PloS one 20170602 6

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 ex ...[more]

PMID: 28574991

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Project description:In this study, we evaluated and forecasted the cumulative opportunities for residents to access radiotherapy services in Cali, Colombia, while accounting for traffic congestion, using a new people-centred methodology with an equity focus. Furthermore, we identified 1-2 optimal locations where new services would maximise accessibility. We utilised open data and publicly available big data. Cali is one of South America's cities most impacted by traffic congestion.MethodologyUsing a people-centred approach, we tested a web-based digital platform developed through an iterative participatory design. The platform integrates open data, including the location of radiotherapy services, the disaggregated sociodemographic microdata for the population and places of residence, and big data for travel times from Google Distance Matrix API. We used genetic algorithms to identify optimal locations for new services. We predicted accessibility cumulative opportunities (ACO) for traffic ranging from peak congestion to free-flow conditions with hourly assessments for 6-12 July 2020 and 23-29 November 2020. The interactive digital platform is openly available.Primary and secondary outcomesWe present descriptive statistics and population distribution heatmaps based on 20-min accessibility cumulative opportunities (ACO) isochrones for car journeys. There is no set national or international standard for these travel time thresholds. Most key informants found the 20-min threshold reasonable. These isochrones connect the population-weighted centroid of the traffic analysis zone at the place of residence to the corresponding zone of the radiotherapy service with the shortest travel time under varying traffic conditions ranging from free-flow to peak-traffic congestion levels. Additionally, we conducted a time-series bivariate analysis to assess geographical accessibility based on economic stratum. We identify 1-2 optimal locations where new services would maximize the 20-min ACO during peak-traffic congestion.ResultsTraffic congestion significantly diminished accessibility to radiotherapy services, particularly affecting vulnerable populations. For instance, urban 20-min ACO by car dropped from 91% of Cali's urban population within a 20-min journey to the service during free-flow traffic to 31% during peak traffic for the week of 6-12 July 2020. Percentages represent the population within a 20-min journey by car from their residence to a radiotherapy service. Specific ethnic groups, individuals with lower educational attainment, and residents on the outskirts of Cali experienced disproportionate effects, with accessibility decreasing to 11% during peak traffic compared to 81% during free-flow traffic for low-income households. We predict that strategically adding sufficient services in 1-2 locations in eastern Cali would notably enhance accessibility and reduce inequities. The recommended locations for new services remained consistent in both of our measurements.These findings underscore the significance of prioritising equity and comprehensive care in healthcare accessibility. They also offer a practical approach to optimising service locations to mitigate disparities. Expanding this approach to encompass other transportation modes, services, and cities, or updating measurements, is feasible and affordable. The new approach and data are particularly relevant for planning authorities and urban development actors.

Dataset Information

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

Publications

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

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