A Data-Driven Approach for Generation of Tactical Planning Rules Regarding Buffer Time in Initial Railway Timetables

A Data-Driven Approach for Generation of Tactical Planning Rules Regarding Buffer Time in Initial Railway Timetables: A Case Study on the Differentiation of Buffer Times in the Railway Timetable of Nederlandse Spoorwegen

Bouman, Susan (TU Delft Civil Engineering and Geosciences)

Goverde, R.M.P. (mentor)
Bešinović, Nikola (mentor)
Fazi, S. (mentor)
Looij, P. (mentor)

Delft University of Technology

Transport, Infrastructure and Logistics

2022-07-01

Despite advanced communication, monitoring, and control facilities, train operations are still subject to uncertainties that can disturb train services, cause delay to multiple trains, and propagate through the network. One option is to mitigate delay propagation in the timetable design by adding buffer time to the minimum difference between the time two successive train of either direction enter a section. It is still common practice to design buffer times based on a deterministic value, decreasing operational capacity and requiring large amount of manual checking by planners. Existing approaches to effectively allocate buffer time in timetables lack flexibility and require an initial timetable. In this paper, a data-driven approach for determination of buffer time planning rules suitable for usage in an initial timetable is presented. These planning rules are not necessarily generic, but rather depend on timetable characteristic. Two metrics that describe delay propagation, mean secondary delay and hindrance percentage, are extracted from literature and predicted in a regression analysis with the use of timetable characteristics related to headway situations of two succeeding trains. The results of the regression analysis on a case study of the Dutch railway network between Haarlem, Leiden Centraal and Schiphol Airport are used to determine the amount of scheduled buffer time that would ensure a certain amount of hindrance percentage given a specific headway situation. The results show that the mean secondary delay and hindrance percentage for various headway situations can both be predicted with an accuracy of 90.7\% based on timetable characteristics and is quite heterogeneous. Mean secondary delay appeared not significantly impacted by the scheduled buffer time, contrary to hindrance percentage which is significantly influenced by the scheduled buffer time.

Railways
Timetable design
Buffer time
Data-driven
Multiple linear regression
Delay propagation
Secondary delay
Hindrance percentage

http://resolver.tudelft.nl/uuid:69aafb5b-9c75-4eee-b968-b30d00d37180

Student theses

master thesis

© 2022 Susan Bouman