Description
In railway scheduling, the planning of time supplements is crucial to the robustness of the resulting timetable. Time supplements as a means to accommodate for train delays are often distributed according to operation rules and based on experience. A part of the project for strategic schedule optimization at DB Netze aims at improving the supplements distribution through learning of structures of delay propagation and transmission from historical railway operation data. The work at hand focuses on delay transmissions between trains. It employs correlations and correlation network analysis to identify and analyze these knock-on delays and to develop logical precedence orders of trains at certain operation points which can in turn be used in a sequential calculation of single train delay propagation. Furthermore, it endeavors to establish a basis to identify strongly connected groups of trains and stations, thus forming relevant subnets for further analysis.
Modeling Delay Propagation and Transmission in Railway Networks
In railway scheduling, the planning of time supplements is crucial to the robustness of the resulting timetable. Time supplements as a means to accommodate for train delays are often distributed according to operation rules and based on experience. A part of the project for strategic schedule optimization at DB Netze aims at improving the supplements distribution through learning of structures of delay propagation and transmission from historical railway operation data. The work at hand focuses on delay transmissions between trains. It employs correlations and correlation network analysis to identify and analyze these knock-on delays and to develop logical precedence orders of trains at certain operation points which can in turn be used in a sequential calculation of single train delay propagation. Furthermore, it endeavors to establish a basis to identify strongly connected groups of trains and stations, thus forming relevant subnets for further analysis.