A robustness assessment with passenger flow data of high-speed rail network in China

Abstract

Many existing studies on the robustness assessment of high-speed rail (HSR) networks use HSR services derived from timetable data as an alternative indicator of actual passenger flow. This alternative method has inherent limitations because of the various capacities and seat allocations among different HSR services operated between the same station pairs, leading to a biased evaluation of network robustness. Using an actual ticket sales dataset and HSR timetable data, this study integrates passenger volume into the robustness evaluation of China's HSR network. Moreover, this study proposes systematic methods to assess the robustness of HSR networks at the station and large-scale network levels, with procedure indicators. We design random failure and target attack simulation scenarios in un-weighted, travel-time-weighted, and flow-weighted networks. Simulation is conducted from 2014 to 2016, and with a medium-and long-term HSR network plan in China. Using the methodological advances and empirical studies, we find that China's HSR network became less robust first, and then enhanced robustness strength during network expansion. The significance of integrating passenger flow data in the robustness assessment is confirmed, as the same attack strategy may affect the total travel time and the number of passengers delivered differently. In addition, the proposed indicators are useful for capturing the procedure failure of HSR networks and critical stations.