Abstract
Railway systems occasionally get into a state of out-of-control, meaning that there is barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. These situations can either be caused by large disruptions or unexpected propagation and accumulation of delays. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing methods cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework aiming at reducing the impact of these situations and - if possible - avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations using tools from complexity science and (ii) a set of rescheduling measures robust against the features of out-of-control situations, using tools from operations research.
Original language | English |
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Title of host publication | Proceedings of CASPT 2018 |
Subtitle of host publication | 23-25 July, Brisbane, Australia |
Number of pages | 19 |
Publication status | Published - 2018 |
Event | Caspt 2018: 14th Conference on Advanced Systems in Public Transport and TransitData 2018 - Brisbane Convention and Exhibition Centre, Brisbane, Australia Duration: 23 Jul 2018 → 25 Jul 2018 Conference number: 14 |
Conference
Conference | Caspt 2018: 14th Conference on Advanced Systems in Public Transport and TransitData 2018 |
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Abbreviated title | CASPT 2018 |
Country/Territory | Australia |
City | Brisbane |
Period | 23/07/18 → 25/07/18 |
Keywords
- Railway disruption management
- Rescheduling
- Complexity science
- Operations research