Open AccessComplex network model for railway timetable stability optimisation
Author(s) -
Meng Xuelei,
Jia Limin,
Xiang Wanli
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5257
Subject(s) - stability (learning theory) , process (computing) , computer science , engineering , operations research , industrial engineering , machine learning , operating system
Recent studies on train timetable are paying more and more attention to the dynamic characteristics. Among the dynamic characteristics, stability is a most important one, which determines the capacity of the train timetable to tolerate the disturbance in the train operation process. In this study, the authors build a complex network model to describe the train timetable, making it possible to utilise the complex network theory to study the train timetable optimisation problem. Then, they design the solving algorithm to solve the problem. Finally, they present a computing case to prove the approach to improve the train timetable stability is practical. The approach proposed in this study can generate referential advice for the railway operators design the train timetable.