Citation

Chen Z, Wang Y, Zhou L. Transp. Policy 2021; 101: 1-13.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.tranpol.2020.11.008

PMID

unavailable

Abstract

High-speed rail (HSR) has become a competitive mode with aviation for medium-distance intercity travel, given the massive deployment of the HSR infrastructure network in China. While the travel experience with both HSR and air has become more convenient, the systems' operational reliability in terms of punctuality remains a key concern, especially during disruptive events, such as under severe weather conditions. Although previous studies have attempted to investigate the impact of severe weather events on the operational performance of transportation systems, there is still a lack of ability to forecast to what extent the performance of different transportation systems may vary under various conditions. This study develops an integrated modeling framework that allows us to predict the performance of weather-induced delays of different transportation systems, including HSR and aviation. By applying machine-learning methods to real-world transportation performance data, the study examines the robustness of the method, variations of data characteristics and the different applications of the predictive modeling system. Overall, the concept and modeling framework provide important implications for the improvement of transportation system resilience to various severe weather-related disruptions through the understanding of the impact and its predictability of the system performance.

Language: en

Keywords

Aviation; Big data; Extreme weather event; High-speed rail; Machine learning