Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1132152 | Transportation Research Part B: Methodological | 2013 | 13 Pages |
Abstract
This paper makes two contributions. It firstly proposes the use of a fault tolerance approach for railway operations and secondly it develops a minimum time gap matrix model for capacity computation and the study of perturbation effects through the generation of a compressed timetable. A fault tolerance approach is proposed to improve the operational efficiency of the railway network in terms of the network capacity and the robustness of train timetables. The term fault tolerance is used in a broad sense, to represent any abnormalities or unexpected events in operations or equipment. Enhanced fault tolerance capability provides safety assurance so that, in normal operating conditions, trains can adopt much faster speed profiles when approaching a 'to-be-cleared' signal block at stations and junctions than those currently permitted, effectively turning the status of 'be ready to stop' to that of 'proceed with caution'. In the rare event of a 'fault' in the system, e.g. if a conflicting train fails to move out of a signalling block as expected or a switch fails to operate as required, the train would be re-routed to take an alternative path. In this study, the new approach is developed on three scenarios i.e., a standard classic right turn junction, a terminus station, and a small network combining both of these elements to demonstrate the performance gains, but the concept can be readily extended for other types of junctions/stations. Results so far show great potential in the proposed fault tolerance approach to increase the capacity and enhance operational robustness to perturbations at such locations. A novel method for capacity computation called minimum time gap matrix model is also introduced that has capability to produce compressed timetables directly from a given train sequence.
Related Topics
Social Sciences and Humanities
Decision Sciences
Management Science and Operations Research
Authors
Ziauddin Ursani, T.X. Mei, Anthony Whiteing,